A Systematic Review About Moral Implications in Autonomous Vehicles Between 2005 and 2023

A Systematic Review About Moral Implications in Autonomous Vehicles Between 2005 and 2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A Systematic Review About Moral Implications in Autonomous Vehicles Between 2005 and 2023 Mohamad Mahdi Davar, MM Khojasteh, Mahdi Zaemi, Shahrzad Mamourian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5442122/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : With the expansion of Artificial Intelligence (AI) in the contemporary era and the emergence of autonomous vehicles as a result, different ethical challenges have also arisen. Further, these challenges can be answered and investigated with different ethical and moral approaches. Therefore, we will find that this is a significant issue and also reviewing the researches that have been done in this regard is also of great importance. Methods : Using the four-steps method to conduct a systematic review, we first extracted related documents by searching for relevant keywords in the Web of Science (WoS) databases, and also conducted a systematic review using the VOSviewer (version 1.6.20). Results: After extracting these documents and using the VOSviewer, active countries in this field have been examined in terms of the number of documents and citations, active journals, active publishers, documents in terms of the number of citations, and also active authors in this field, as well as keywords and terms. Artificial Intelligence and Machine Learning Philosophy Ethics Artificial Intelligence AI Autonomous Vehicles VOSviewer. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Artificial Intelligence (AI) has had a significant impact across various sectors and industries (Collins, 2021), like medicine (Rajpurkar et al. 2022), healthcare (Longoni et al. 2019), humanities (Kabali Samartoiy& Davar, 2023; Davar& Hosseini, 2024), and education (Memarian& Doleck, 2023; Kousa& Niemi, 2023), and it has also now entered people's lives (Zhang et al. 2022). As a matter of fact, defining AI is not easy (Sheikh et al. 2023). Although defining artificial intelligence is challenging, McCarthy (2007) said the comprehensive definition: “is the science and engineering of making intelligent machines, especially intelligent computer programs”. In other phrases, AI is a process or system with using from “a computer that can simulate intelligent human behavior with technological innovations such as machine learning, natural language processing, and neural networks” (Su et al. 2022). The fact is our world is confronted with a number of moral dilemmas and ethical challenges, and it is necessary to practice our moral reasoning (Bickley& Torgler, 2023). In addition, morality one of the fundamental and debatable in any aspects, and in dealing with moral dilemmas and ethical challenges, a moral problem can be classified utilitarian versus deontological moral views (Zhang et al. 2022). We can categorize moral preferences into two types for deontologists: moral absolutism and moderate deontologism, and we can also categorize two versions of consequentialism or utilitarianism, strict and moderate (Feess et al. 2022). Consequences play an important role in utilitarianism (Mill, 2016), while moral duty plays a pivotal role in deontological moral perspective (Kant, 2017). According this, we have two glasses and different approaches to view a moral phenomenon or a moral dilemma, such as trolley problem in autonomous and driverless vehicles. Albeit autonomous vehicles have a number of merits, they have many challenges (Cunneen et al. 2020). The truth is the trolley problem is a well-known thought experiment of philosophical and ethical scenarios where a runaway trolley is heading towards five people on the tracks, and the only way to save them is by diverting the trolley to a different track where one person would be sacrificed (Nyholm& Smids, 2016). It should be noted that Lin (2015) claimed that one of the most iconic ethical thought-experiments is the trolley problem, which could potentially become a real-life scenario with the advent of autonomous vehicles (Also see Nyholm& Smids, 2016). Various researches has been conducted on this subject, and in this study, we have selected 200 papers (articles and review articles) and systematically review them. In addition, among these 200 studies related to the topic in terms of (title, abstract, and keywords), we review 107 articles that have at least 5 citations as a content review. For this reason, our study is a content review and mapping review. In the content review, we will examine 107 selected articles and present their content in tables. Additionally, in the mapping review, country of co-authorships, the distribution of journals, top documents, top authors, the most 180 day usage articles, keywords, text data has been analyzed using the VOSviewer. The main goal of this study is to provide suggestions for future researches on this topic, using a systematic review of existing studies. It is likely that autonomous vehicles will expand in the future (Geisslinger et al. 2021), and it is also incontrovertible that with the growth of AI and driverless vehicles, their ethical challenges also increase, and the likelihood of the "trolley problem" becoming a reality also increases (Nyholm& Smids, 2016). It can, therefore, be argued that by reviewing previous researches, study suggestions can be made for the future to resolve this moral dilemma. Methodology A systematic literature review is composed by four major steps: 1. Literature retrieval; 2. Literature screening; 3. Content analysis; 4. Bibliometric analysis (Al- Naqbi et al. 2024). First Step: For conducting the literature, we used from the Web of Science (WoS) databases on March 8, 2024. The Web of Science contains a number of bibliographic information (Kumpulainen& Seppänen, 2022; Li et al. 2018). In the first step, we used these keywords: (“self- driving car” OR “autonomous vehicle” OR “driverless vehicle” OR “autonomous car" OR “driverless car” OR “autonomous driving” OR “autonomous systems” (Topic)) AND (“trolley problem” OR “trolley dilemma” OR “trolley issue” OR “ethic*” OR moral*” OR “ethical issue” OR “moral challenges” OR “ethical challenges” OR “moral philosophy” OR “AI ethics” OR “ethical analysis” OR “ethical frameworks” OR “ethical considerations” OR “technological ethics” OR “ethical decision making” OR “ethical dilemma” OR “moral dilemma” OR “moral implications” (Topic)). 1,603 results were found after this search. Then, in next step for organization of our analysis, 727 results were eliminated by these keywords: NOT (“medical ethics” OR “nursing” OR “medicine” OR “healthcare” OR “management*” OR “business” OR “politic*” OR “social” OR “biology” (Topic)). As a result, 876 documents were remained. Second Step: In addition to literature retrieval, this investigation was limited to articles and review articles between 2005 and 2023 (576 documents remained after this limitation), and was also limited to English papers (525 articles remained after this filter), and it should be noted that to increase accuracy we selected three WoS Categories: Philosophy; Ethics; and Computer Science Artificial Intelligence. After these two steps, 200 publications remained in the analysis. The search process is illustrated in Fig. 1 . Third Step: In the content analysis step, 107 papers from 200 papers were examined, and the most important reason for choosing these 107 articles is they have been cited by other authors at least 5 times, and as a result, these documents were selected to analyze this study. Fourth Step: Bibliometric analysis is a quantitative method used by researchers worldwide to study trends in various fields of knowledge and science. It uses statistical techniques to examine the distributed structure of information within documents and the quantitative relationships and dynamic patterns between them. Bibliometric analysis allows scholars to explore the development of fundamental science and technology by analyzing the features and patterns within academic literature (Kalantari et al. 2023). In addition to quantitative analysis of research publications, bibliometric techniques are also used to review literature through content analysis qualitatively. This dual approach helps address the imbalance between quantitative metrics and qualitative assessments in evaluating academic papers. More holistic bibliometric reviews incorporate both numerical indicators and thematic insights from publications (Zhang& Ayes Begum, 2021). In fact, the bibliometric method provides scholars and researchers with a summarized, quantitative way to analyze previous studies on a given topic. It has been recognized in academia and applied in many literature reviews due to its ability to visually depict results drawn from large datasets. By collecting and examining data from several articles, bibliometric tools can examine emerging topics, influential authors and institutions, and evolving trends within a research field - insights that traditional review methods struggle to glean from such extensive sources. Performance analysis, network mapping, and other bibliometric techniques offer researchers comprehensive overviews of frontier scholarship and hot research areas (Zhang et al. 2024). According to the nature of bibliometric analysis, we investigated academic documents with this method. In doing so, due to the present study is based on content, and bibliometric analysis, it is considered a comprehensive literature review. Analysis Tools To analyze the collected data from the WoS Core Collection, this paper used VOSviewer (version 1.6.20). VOSviewer is a software tool created for building and examining bibliometric networks. This program is freely accessible for those conducting bibliometric research. VOSviewer allows developing maps of authors or publications predicated on co-citation information or constructing maps of keywords based on co-occurrence data (van Eck& Waltman, 2010). VOSviewer is useful to identify the most collaborative countries, journals, authors, and keywords, with using some features of VOSviewer like citation, co-authorship, and co-occurrences (Sarker& Bartok, 2024). It should also be noted that this study used from these features for analyzing the data from Web of Science Core Collection. Moreover, we used Microsoft Visio (Micro-soft Inc., Redmond, WA, USA) for make a flowchart as can be seen in Fig. 1 . The Results and Discussion Characterization of Research Publications The total of given publications (200 documents) is limited into 2 categories and demonstrated in Fig. 2 . As a matter of fact, 98.5% is composed by articles (197 documents) and only 1.5% is related to review articles (3 documents). Albeit English papers are selected for the study. Co-Occurrence Map Based on Country of Co-Authorship This study uses a co-occurrence map method based on country co-authorships to understand the scientific relationships and connections between countries. 39 countries contributed publications, and 18 met the were not connected to each other, reducing the threshold of a minimum of 3 publications. However, some countries in this network number of linked countries in the network to 14. England, Germany, and the USA had by far the most connections to other countries in terms of co-authored publications as can be seen in Fig. 3 . As can be seen in Table. 1, England, Germany, and the USA were the top three countries based on link strength, with 12, 10, and 8 respectively. Norway and the People R China each had a link strength of 7. Luxembourg, Spain, Belgium, Canada, and Denmark ranked 6th to 10th. Table No. 1 Top 10 Countries Based on Link Strength Country Documents Citations Total link strength 1 England 37 734 12 2 Germany 22 490 10 3 USA 41 517 8 4 Norway 3 104 7 5 People R China 7 98 7 6 Luxembourg 3 17 5 7 Spain 6 45 5 8 Belgium 3 10 2 9 Canada 6 42 2 10 Denmark 3 18 2 Based on the data in the table. 2, the USA was top according the number of publications ( 41 ) and citations (517). Following the USA are England with 37 publications and 734 citations, and Netherlands with 27 publications and 461 citations. It should be noted that, England had the highest number of citations among all countries. Germany ranked fourth with 22 publications and 490 citations. Italy was the fifth with 11 documents and 111 citations. Australia and Sweden both had the same number of publications ( 9 ), but Australia had 87 citations compared to Sweden's 206. People's Republic of China had seven documents with 98 citations. Canada and Spain each had 12 publications, with Canada having 42 citations and Spain having 45 citations. Table No. 2 Top 10 Countries Based on Total Publications Countries Documents Citations 1 USA 41 517 2 England 37 734 3 Netherlands 27 461 4 Germany 22 490 5 Italy 11 111 6 Australia 9 87 7 Sweden 9 206 8 People R China 7 98 9 Canada 6 42 10 Spain 6 45 Co-Occurrence Map Based on Active Journals in this Field According to Fig. 4 and table no. 3 four journals of Science and Engineering Ethics , Ethical Theory and Moral Practice , Ethics and Information Technology , and Ai& Society were the top in regard to link strength. Moreover, according to the number of publications the journals of Ethics and Information Technology , Ai& Society , and Science and Engineering Ethics were the first top three journals. In addition to this and according to the citations, the journal of Science and Engineering Ethics with 535 citations was the first and the journal of Ethics and Information Technology with 443 citations was the second. Table No. 3 Top 10 Journals Based on Link Strength Journal Documents Citations Total link strength 1 Science and Engineering Ethics 17 535 70 2 Ethical Theory and Moral Practice 8 311 51 3 Ethics and Information Technology 33 443 51 4 Ai& Society 24 194 44 5 Journal of Applied Philosophy 6 53 21 6 Minds and Machines 5 12 19 7 Ethics& International Affairs 8 72 13 8 International Journal of Technoethics 4 17 9 9 Applied Artificial Intelligence 3 53 6 10 Artificial Intelligence 6 107 3 It should be noted that according to these ten active journals, five of them are belonged to Springer (Ethics and Information Technology, Ai& Society, Science and Engineering Ethics, Ethical Theory and Moral Practice, Minds and Machines), and the top three journals according to the citations are also related to Springer as well as number of documents, while the first journal in regard to IF is Artificial Intelligence with 14.4 IF, is related to Elsevier. Table No. 4 Top 10 Journals Based on IF, Total Publications, and Citations Journal Publisher IF Documents Citations Ethics and Information Technology Springer 3.6 ( 4 ) 33 ( 1 ) 443 ( 2 ) Ai& Society Springer 3 ( 5 ) 24 ( 2 ) 194 ( 4 ) Science and Engineering Ethics Springer 3.7 ( 3 ) 17 ( 3 ) 535 ( 1 ) Ethical Theory and Moral Practice Springer 1 ( 10 ) 8 ( 4 ) 311 ( 3 ) Ethics& International Affairs Cambridge University Press 1.3 ( 8 ) 8 ( 5 ) 72 ( 6 ) Artificial Intelligence Elsevier 14.4 ( 1 ) 6 ( 6 ) 107 ( 5 ) Journal of Applied Philosophy WILEY 1.1 ( 9 ) 6 ( 7 ) 53 ( 8 ) Minds and Machines Springer 7.4 ( 2 ) 5 ( 8 ) 12 ( 10 ) International Journal of Technoethics IGI Global 1.4 ( 7 ) 4 ( 9 ) 17 ( 9 ) Applied Artificial Intelligence Taylor & Francis 2.8 ( 6 ) 3 ( 10 ) 53 ( 7 ) Co-Occurrence Map and Table Based on References and Authors With focusing on Fig. 5 and table no. 5 we can realize that the document related to Nyholm& Smids (2016) with 151 citations is the first, and following it the document related to Hevelke& Nida-Rumelin (2015) with 133 citations, and the document of Gogoll& Mueller (2017) with 107 citations are the second and third respectively. Table No. 5 Top 10 References References Citations 1 Nyholm& Smids (2016) 151 2 Hevelke& Nida-Rumelin (2015) 133 3 Gogoll& Mueller (2017) 107 4 Dennis et al. (2016) 94 5 Guglielmo et al. (2009) 91 6 Asaro (2006) 82 7 Nyholm (2018) 76 8 Torrance (2008) 64 9 Chen et al. (2023) 62 10 Sparrow (2016) 60 Further, Nyhol, Sven is the first active author according to both the number of publications and citations. Following them, Smids, Jilles is the second according to citations with 2 publications and Nida- Rumelin, Julian has 133 citations (rank 3) with just 1 publication. Table No. 6 Top 10 Authors Author Documents Citations 1 Nyhol, Sven 6 ( 1 ) 287 ( 1 ) 2 Smids, Jilles 2 ( 3 ) 160 ( 2 ) 3 Nida- Rumelin, Julian 1 ( 4 ) 133 ( 3 ) 4 Gogoll, Jan 4 ( 2 ) 117 ( 4 ) 5 Mueller, Julian F 2 ( 3 ) 112 ( 5 ) 6 Slavkovik, Marija 2 ( 3 ) 94 ( 6 ) 7 Dennis, Louise 1 ( 4 ) 94 ( 6 ) 8 Fisher, Michael 1 ( 4 ) 94 ( 6 ) 9 Webster, Matt 1 ( 4 ) 94 ( 6 ) 10 Guglielmo, Steve 1 ( 4 ) 91 ( 7 ) Co-Occurrence Map Based on Total Link Strength As can be seen from Fig. 6 the network between author in this field is very limited and it is divided into two groups. Chen, Long, Wng, Fei- Yue, and Wang, Fei- Yue with 2 number of documents and 66 citations have 21 link strength. Authors from rank 4th to 16th have 15 link strength and Poszler, Franziska in the 17th place has 3 publications with 26 citations and 10 link strength. Following them, Blind, Felix, Dittmer, Anke, and Faulhaber, Anja K have 1 document with 55 citations and 8 link strength. Table No. 7 Top 20 link strength Author Documents Citations Total Link Strength 1 Chen, Long 2 66 21 2 Wang, Fei- Yue 2 66 21 3 Cao, Dongpu 1 61 15 4 Hu, Zhongxu 1 61 15 5 Huang, Chao 1 61 15 6 Li, Bai 1 61 15 7 Li, Li 1 61 15 8 Li, Yuchen 1 61 15 9 Li, Zixuan 1 61 15 10 Lv, Chen 1 61 15 11 Na, Xiaoxiang 1 61 15 12 Teng, Siyu 1 61 15 13 Tian, Daxin 1 61 15 14 Wang, Jinjun 1 61 15 15 Xing, Yang 1 61 15 16 Zheng, Nanning 1 61 15 17 Poszler, Franziska 3 26 10 18 Blind, Felix 1 55 8 19 Dittmer, Anke 1 55 8 20 Faulhaber, Anja K 1 55 8 The Most 180 Days Usage The document by Chen et al. 2023 with 61 TC has the most usage in 180 days with 132. Following them the document by Gebru et al. 2022 with 12 TC is the second place with number of 44. Other 13 documents are between 10 to 24. Table No. 8 Top 20 180 Days Usage Counts Keywords TC 180 days usage counts Journal Year References 1 Autonomous vehicles; Planning; Vehicle dynamics; Heuristic algorithms; Object detection; Location awareness; Task analysis; Survey of surveys; milestones; autonomous driving; intelligent vehicles 61 132 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023 Chen et al. 2023 2 Particle measurements; Atmospheric measurements; CalibrationTask analysis; Reliability; Ethics; Automation; Human-machine trust; trust measurement; trust calibration; machine trustworthiness 12 44 IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2022 Gebru et al. 2022 3 Blockchain connected and autonomousvehicles; cybersecurity; deep learning; federated learning; internet of vehicles 1 24 WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2024 Ahmad et al. 2024 4 Trolley 8 15 NATURE MACHINE INTELLIGENCE 2023 Geisslinger et al. 2023 5 Autonomous Vehicles; Trolley Problem; Self-driving cars; Ethical naturalism; Responsibility; Policy 3 14 MINDS AND MACHINES 2022 Arfini et al. 2022 6 Artificial intelligence; Automation; Engineering ethics; Self-driving cars; Socio-technical systems 37 13 SCIENCE AND ENGINEERING ETHICS 2017 Borenstein et al. 2019 7 Data; Bias; Ethics; Artificial Intelligence; Machine Translation 12 13 ETHICS AND INFORMATION TECHNOLOGY 2021 Tomalin et al. 2021 8 Acceptance model; cybersecurity; human-centered artificial intelligence (HAI); human-robot interaction (HRI); performance of robots and autonomous system (RAS); safety; system health; trustiness; trustworthiness of RAS; worthiness 10 12 IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022 He et al. 2022 9 Moral Responsibility; Robots; ATTRIBUTABILITY; ACCOUNTABILITY; ANSWERABILITY; RETRIBUTION; Respect 8 14 ETHICS AND INFORMATION TECHNOLOGY 2022 Königs 2022 10 Decision-making; making process; Responsible AI; Intelligence cycle 1 12 ETHICS AND INFORMATION TECHNOLOGY 2023 Meerveld et al. 2023 11 Ethics; Ethical; Moral; Autonomous car; Self-driving car; Responsibility; Liability; Duty to intervene 148 11 SCIENCE AND ENGINEERING ETHICS 2014 Hevelke& Nida-Rümelin, 2015 12 Autonomous driving; Automation; Ethics; Morality; Dilemma 113 11 SCIENCE AND ENGINEERING ETHICS 2017 Gogoll& Müller, 2017 13 Self-driving; Autonomous vehicles; Automated vehicles; Ethics; Morals; Value systems; Artificial intelligence 5 11 AI & SOCIETY 2021 Siegel& Pappas, 2023 14 Artificial intelligence; Machine ethics; AI alignment; Autonomous AI; Trustworthiness 1 10 ETHICS AND INFORMATION TECHNOLOGY 2023 Firt, 2023 15 Autonomous systems; Meaningful human control; Security; Decision-making; Human-Machine-Interaction 3 10 ETHICS AND INFORMATION TECHNOLOGY 2023 Christen et al. 2023 Co-Occurrence Map Based on Keywords According to Fig. 7 and Fig. 8 and also table no. 9, the most occurrence term is "car" with relevance score of 0.86. the term of " Self " with the highest relevance score (0.95) was the second with 72 occurrences. Following them, the terms of "Aws" and "Argument" are in the fourth and fifth places with the relevance score of 62 and 57 respectively. Furthermore, it can be seen that from fig no. 9 and table no. 10 the keywords of "Ethics" and "Artificial Intelligence" are the most relevant keywords in this field, with the occurrences of 44 and 31 respectively. In addition, these two keywords have 144 and 121 link strength respectively. Following them, the keywords of "Autonomous Vehicles" and "Responsibility" with 28 and 23 occurrences and 93 and 82 link strength respectively are the most used by researchers in these related fields. Interestingly, both "Robots" and "Self- driving cars" 17 times occurred in the researches. Table No. 9 Top Terms According to Relevance Score Term Occurrences Relevance Score 1 Car 100 0.86 2 Self 72 0.95 3 Aws 62 0.87 4 Argument 57 0.67 5 Framework 49 0.62 6 Gap 48 0.26 7 Intelligence 41 0.27 8 Harm 38 0.63 9 Risk 38 0.57 10 Study 37 0.49 Table No. 10 Top Keywords Keyword Occurrences Total Link Strength 1 Ethics 44 144 2 Artificial Intelligence 31 121 3 Autonomous Vehicles 28 93 4 Responsibility 23 82 5 Robots 17 63 6 Self- driving cars 17 51 7 Machine ethics 18 46 8 Autonomy 16 43 9 Trolley 10 43 10 Automation 9 35 Conclusion The current review included four steps 1. Literature retrieval; 2. Literature screening; 3. Content analysis; 4. Bibliometric analysis. In the first stage, we obtained 876 scientific documents using the Web of Science database with using related keywords. In the next step, we limited the found scientific documents to the years 2005 to 2023 and also limited this search only to documents in English. In addition, we only selected scientific documents in the categories of philosophy, ethics, computer science, and artificial intelligence, and after applying these limitations, we received 200 scientific documents for the review. In the next stage, we selected 107 articles for this review. The reason for choosing these 107 articles was that each document should have been cited at least 5 times. In the fourth stage, which is the systematic review stage, we investigated the number of documents, citations, and link strength based on countries, journals, documents, authors, and keywords using the VOSviewer (version 1.6.20). Further, we investigated active publishers in this field and documents that were used the most in the last 180 days. In this field, the most scientific documents are related to the United States of America, and the highest number of citations are related to the England. In relation to journals, the highest number of scientific documents is related to Ethics and Information Technology and the highest number of citations is related to Science and Engineering Ethics . In this regard, the highest impact factor is related to the Artificial Intelligence , which is published by Elsevier. However, Springer Publications has the most active journals in this field. In addition, the highest scientific document in this field has 151 citations and also the most active author in this field has 6 scientific documents and 287 citations. The document that have been used the most in the last 180 days are related to IEEE Transactions on Intelligent Vehicles . On the other hand, the terms "car" and "self" have the most occurrences, and the keywords "ethics" and "artificial intelligence" also have the most occurrences. References Ahmad, J., Zia, M. U., Naqvi, I. H., Chattha, J. N., Butt, F. A., Huang, T., & Xiang, W. (2024). 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"Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review" Sustainability 16(3) 1166. https://doi.org/10.3390/su16031166 Nyholm, S. (2018). Attributing Agency to Automated Systems: Reflections on Human–Robot Collaborations and Responsibility-Loci. Sci Eng Ethics 24, 1201–1219. https://doi.org/10.1007/s11948-017-9943-x Nyholm, S., Smids, J. (2016). The Ethics of Accident-Algorithms for Self-Driving Cars: an Applied Trolley Problem?. Ethic Theory Moral Prac 19, 1275–1289. https://doi.org/10.1007/s10677-016-9745-2 Rajpurkar, P., Chen, E., Banerjee, O. et al. (2022) AI in health and medicine. Nat Med 28, 31–38. https://doi.org/10.1038/s41591-021-01614-0 Sarker, M. S. I., & Bartok, I. (2024). Global trends of green manufacturing research in the textile industry using bibliometric analysis. Case Studies in Chemical and Environmental Engineering , 9, 100578. https://doi.org/10.1016/j.cscee.2023.100578 Sheikh H, Prins C, Schrijvers E. (2023) Artificial Intelligence: Definition and Background. Mission AI . 21(1):15-41. doi:10.1007/978-3-031-21448-6_2 Siegel, J., Pappas, G. (2023) Morals, ethics, and the technology capabilities and limitations of automated and self-driving vehicles. AI & Soc 38, 213–226. https://doi.org/10.1007/s00146-021-01277-y Sparrow, R. (2016). Robots and Respect: Assessing the Case Against Autonomous Weapon Systems. Ethics & International Affairs , 30(1), 93–116. doi:10.1017/S0892679415000647. Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence , 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065 Tomalin, M., Byrne, B., Concannon, S. et al. (2021). The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing. Ethics Inf Technol 23, 419–433. https://doi.org/10.1007/s10676-021-09583-1 Torrance, S. (2008). Ethics and consciousness in artificial agents. AI & Soc 22, 495–521. https://doi.org/10.1007/s00146-007-0091-8 van Eck, N.J., Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3 Zhang, Ke, and Ayse Begum Aslan. (2021). AI technologies for education: Recent research & future directions, Computers and Education : Artificial Intelligence 2, 100025. Zhang, S., Wang, X., Xu, J., Chen, Q., Peng, M., & Hao, J. (2024). Green manufacturing for achieving carbon neutrality goal requires innovative technologies: A bibliometric analysis from 1991 to 2022. Journal of environmental sciences (China) , 140, 255–269. https://doi.org/10.1016/j.jes.2023.08.016 Zhang, Z., Chen, Z., & Xu, L. (2022). Artificial intelligence and moral dilemmas: Perception of ethical decision-making in AI. Journal of Experimental Social Psychology , 101, 104327. Footnotes Impact Factor Times Cited Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5442122","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":377362116,"identity":"1e0b6539-c082-4468-b854-4459c88f67b0","order_by":0,"name":"Mohamad Mahdi 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medicine (Rajpurkar et al. 2022), healthcare (Longoni et al. 2019), humanities (Kabali Samartoiy\u0026amp; Davar, 2023; Davar\u0026amp; Hosseini, 2024), and education (Memarian\u0026amp; Doleck, 2023; Kousa\u0026amp; Niemi, 2023), and it has also now entered people's lives (Zhang et al. 2022). As a matter of fact, defining AI is not easy (Sheikh et al. 2023). Although defining artificial intelligence is challenging, McCarthy (2007) said the comprehensive definition: \u0026ldquo;is the science and engineering of making intelligent machines, especially intelligent computer programs\u0026rdquo;. In other phrases, AI is a process or system with using from \u0026ldquo;a computer that can simulate intelligent human behavior with technological innovations such as machine learning, natural language processing, and neural networks\u0026rdquo; (Su et al. 2022).\u003c/p\u003e \u003cp\u003eThe fact is our world is confronted with a number of moral dilemmas and ethical challenges, and it is necessary to practice our moral reasoning (Bickley\u0026amp; Torgler, 2023). In addition, morality one of the fundamental and debatable in any aspects, and in dealing with moral dilemmas and ethical challenges, a moral problem can be classified utilitarian versus deontological moral views (Zhang et al. 2022). We can categorize moral preferences into two types for deontologists: moral absolutism and moderate deontologism, and we can also categorize two versions of consequentialism or utilitarianism, strict and moderate (Feess et al. 2022). Consequences play an important role in utilitarianism (Mill, 2016), while moral duty plays a pivotal role in deontological moral perspective (Kant, 2017). According this, we have two glasses and different approaches to view a moral phenomenon or a moral dilemma, such as trolley problem in autonomous and driverless vehicles. Albeit autonomous vehicles have a number of merits, they have many challenges (Cunneen et al. 2020). The truth is the trolley problem is a well-known thought experiment of philosophical and ethical scenarios where a runaway trolley is heading towards five people on the tracks, and the only way to save them is by diverting the trolley to a different track where one person would be sacrificed (Nyholm\u0026amp; Smids, 2016). It should be noted that Lin (2015) claimed that one of the most iconic ethical thought-experiments is the trolley problem, which could potentially become a real-life scenario with the advent of autonomous vehicles (Also see Nyholm\u0026amp; Smids, 2016).\u003c/p\u003e \u003cp\u003eVarious researches has been conducted on this subject, and in this study, we have selected 200 papers (articles and review articles) and systematically review them. In addition, among these 200 studies related to the topic in terms of (title, abstract, and keywords), we review 107 articles that have at least 5 citations as a content review. For this reason, our study is a content review and mapping review. In the content review, we will examine 107 selected articles and present their content in tables. Additionally, in the mapping review, country of co-authorships, the distribution of journals, top documents, top authors, the most 180 day usage articles, keywords, text data has been analyzed using the VOSviewer.\u003c/p\u003e \u003cp\u003eThe main goal of this study is to provide suggestions for future researches on this topic, using a systematic review of existing studies. It is likely that autonomous vehicles will expand in the future (Geisslinger et al. 2021), and it is also incontrovertible that with the growth of AI and driverless vehicles, their ethical challenges also increase, and the likelihood of the \"trolley problem\" becoming a reality also increases (Nyholm\u0026amp; Smids, 2016). It can, therefore, be argued that by reviewing previous researches, study suggestions can be made for the future to resolve this moral dilemma.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eA systematic literature review is composed by four major steps: 1. Literature retrieval; 2. Literature screening; 3. Content analysis; 4. Bibliometric analysis (Al- Naqbi et al. 2024).\u003c/p\u003e \u003cp\u003eFirst Step: For conducting the literature, we used from the Web of Science (WoS) databases on March 8, 2024. The Web of Science contains a number of bibliographic information (Kumpulainen\u0026amp; Sepp\u0026auml;nen, 2022; Li et al. 2018). In the first step, we used these keywords: (\u0026ldquo;self- driving car\u0026rdquo; OR \u0026ldquo;autonomous vehicle\u0026rdquo; OR \u0026ldquo;driverless vehicle\u0026rdquo; OR \u0026ldquo;autonomous car\" OR \u0026ldquo;driverless car\u0026rdquo; OR \u0026ldquo;autonomous driving\u0026rdquo; OR \u0026ldquo;autonomous systems\u0026rdquo; (Topic)) AND (\u0026ldquo;trolley problem\u0026rdquo; OR \u0026ldquo;trolley dilemma\u0026rdquo; OR \u0026ldquo;trolley issue\u0026rdquo; OR \u0026ldquo;ethic*\u0026rdquo; OR moral*\u0026rdquo; OR \u0026ldquo;ethical issue\u0026rdquo; OR \u0026ldquo;moral challenges\u0026rdquo; OR \u0026ldquo;ethical challenges\u0026rdquo; OR \u0026ldquo;moral philosophy\u0026rdquo; OR \u0026ldquo;AI ethics\u0026rdquo; OR \u0026ldquo;ethical analysis\u0026rdquo; OR \u0026ldquo;ethical frameworks\u0026rdquo; OR \u0026ldquo;ethical considerations\u0026rdquo; OR \u0026ldquo;technological ethics\u0026rdquo; OR \u0026ldquo;ethical decision making\u0026rdquo; OR \u0026ldquo;ethical dilemma\u0026rdquo; OR \u0026ldquo;moral dilemma\u0026rdquo; OR \u0026ldquo;moral implications\u0026rdquo; (Topic)). 1,603 results were found after this search. Then, in next step for organization of our analysis, 727 results were eliminated by these keywords: NOT (\u0026ldquo;medical ethics\u0026rdquo; OR \u0026ldquo;nursing\u0026rdquo; OR \u0026ldquo;medicine\u0026rdquo; OR \u0026ldquo;healthcare\u0026rdquo; OR \u0026ldquo;management*\u0026rdquo; OR \u0026ldquo;business\u0026rdquo; OR \u0026ldquo;politic*\u0026rdquo; OR \u0026ldquo;social\u0026rdquo; OR \u0026ldquo;biology\u0026rdquo; (Topic)). As a result, 876 documents were remained.\u003c/p\u003e \u003cp\u003eSecond Step: In addition to literature retrieval, this investigation was limited to articles and review articles between 2005 and 2023 (576 documents remained after this limitation), and was also limited to English papers (525 articles remained after this filter), and it should be noted that to increase accuracy we selected three WoS Categories: Philosophy; Ethics; and Computer Science Artificial Intelligence. After these two steps, 200 publications remained in the analysis. The search process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThird Step: In the content analysis step, 107 papers from 200 papers were examined, and the most important reason for choosing these 107 articles is they have been cited by other authors at least 5 times, and as a result, these documents were selected to analyze this study.\u003c/p\u003e \u003cp\u003eFourth Step: Bibliometric analysis is a quantitative method used by researchers worldwide to study trends in various fields of knowledge and science. It uses statistical techniques to examine the distributed structure of information within documents and the quantitative relationships and dynamic patterns between them. Bibliometric analysis allows scholars to explore the development of fundamental science and technology by analyzing the features and patterns within academic literature (Kalantari et al. 2023). In addition to quantitative analysis of research publications, bibliometric techniques are also used to review literature through content analysis qualitatively. This dual approach helps address the imbalance between quantitative metrics and qualitative assessments in evaluating academic papers. More holistic bibliometric reviews incorporate both numerical indicators and thematic insights from publications (Zhang\u0026amp; Ayes Begum, 2021). In fact, the bibliometric method provides scholars and researchers with a summarized, quantitative way to analyze previous studies on a given topic. It has been recognized in academia and applied in many literature reviews due to its ability to visually depict results drawn from large datasets. By collecting and examining data from several articles, bibliometric tools can examine emerging topics, influential authors and institutions, and evolving trends within a research field - insights that traditional review methods struggle to glean from such extensive sources. Performance analysis, network mapping, and other bibliometric techniques offer researchers comprehensive overviews of frontier scholarship and hot research areas (Zhang et al. 2024). According to the nature of bibliometric analysis, we investigated academic documents with this method. In doing so, due to the present study is based on content, and bibliometric analysis, it is considered a comprehensive literature review.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Tools\u003c/h2\u003e \u003cp\u003eTo analyze the collected data from the WoS Core Collection, this paper used VOSviewer (version 1.6.20). VOSviewer is a software tool created for building and examining bibliometric networks. This program is freely accessible for those conducting bibliometric research. VOSviewer allows developing maps of authors or publications predicated on co-citation information or constructing maps of keywords based on co-occurrence data (van Eck\u0026amp; Waltman, 2010).\u003c/p\u003e \u003cp\u003eVOSviewer is useful to identify the most collaborative countries, journals, authors, and keywords, with using some features of VOSviewer like citation, co-authorship, and co-occurrences (Sarker\u0026amp; Bartok, 2024). It should also be noted that this study used from these features for analyzing the data from Web of Science Core Collection. Moreover, we used Microsoft Visio (Micro-soft Inc., Redmond, WA, USA) for make a flowchart as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"The Results and Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of Research Publications\u003c/h2\u003e\n \u003cp\u003eThe total of given publications (200 documents) is limited into 2 categories and demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. As a matter of fact, 98.5% is composed by articles (197 documents) and only 1.5% is related to review articles (3 documents). Albeit English papers are selected for the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCo-Occurrence Map Based on Country of Co-Authorship\u003c/h3\u003e\n\u003cp\u003eThis study uses a co-occurrence map method based on country co-authorships to understand the scientific relationships and connections between countries. 39 countries contributed publications, and 18 met the were not connected to each other, reducing the threshold of a minimum of 3 publications. However, some countries in this network number of linked countries in the network to 14. England, Germany, and the USA had by far the most connections to other countries in terms of co-authored publications as can be seen in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAs can be seen in Table. 1, England, Germany, and the USA were the top three countries based on link strength, with 12, 10, and 8 respectively. Norway and the People R China each had a link strength of 7. Luxembourg, Spain, Belgium, Canada, and Denmark ranked 6th to 10th.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cem\u003eTable No. 1 Top 10 Countries Based on Link Strength\u003c/em\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal link strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeople R China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuxembourg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the data in the table. 2, the USA was top according the number of publications (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e) and citations (517). Following the USA are England with 37 publications and 734 citations, and Netherlands with 27 publications and 461 citations. It should be noted that, England had the highest number of citations among all countries. Germany ranked fourth with 22 publications and 490 citations. Italy was the fifth with 11 documents and 111 citations. Australia and Sweden both had the same number of publications (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e), but Australia had 87 citations compared to Sweden\u0026apos;s 206. People\u0026apos;s Republic of China had seven documents with 98 citations. Canada and Spain each had 12 publications, with Canada having 42 citations and Spain having 45 citations.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cem\u003eTable No. 2 Top 10 Countries Based on Total Publications\u003c/em\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeople R China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eCo-Occurrence Map Based on Active Journals in this Field\u003c/h3\u003e\n\u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and table no. 3 four journals of \u003cem\u003eScience and Engineering Ethics\u003c/em\u003e, \u003cem\u003eEthical Theory and Moral Practice\u003c/em\u003e, \u003cem\u003eEthics and Information Technology\u003c/em\u003e, and Ai\u0026amp; Society were the top in regard to link strength. Moreover, according to the number of publications the journals of \u003cem\u003eEthics and Information Technology\u003c/em\u003e, \u003cem\u003eAi\u0026amp; Society\u003c/em\u003e, and \u003cem\u003eScience and Engineering Ethics\u003c/em\u003e were the first top three journals. In addition to this and according to the citations, the journal of \u003cem\u003eScience and Engineering Ethics\u003c/em\u003e with 535 citations was the first and the journal of \u003cem\u003eEthics and Information Technology\u003c/em\u003e with 443 citations was the second.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cem\u003eTable No. 3 Top 10 Journals Based on Link Strength\u003c/em\u003e\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal link strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScience and Engineering Ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Theory and Moral Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics and Information Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAi\u0026amp; Society\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Applied Philosophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinds and Machines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics\u0026amp; International Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Journal of Technoethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApplied Artificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIt should be noted that according to these ten active journals, five of them are belonged to Springer (Ethics and Information Technology, Ai\u0026amp; Society, Science and Engineering Ethics, Ethical Theory and Moral Practice, Minds and Machines), and the top three journals according to the citations are also related to Springer as well as number of documents, while the first journal in regard to IF is Artificial Intelligence with 14.4 IF, is related to Elsevier.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cem\u003eTable No. 4 Top 10 Journals Based on IF, Total Publications, and Citations\u003c/em\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublisher\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics and Information Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpringer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e443 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAi\u0026amp; Society\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpringer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScience and Engineering Ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpringer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Theory and Moral Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpringer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics\u0026amp; International Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCambridge University Press\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElsevier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Applied Philosophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWILEY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinds and Machines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpringer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Journal of Technoethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIGI Global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApplied Artificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaylor \u0026amp; Francis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCo-Occurrence Map and Table Based on References and Authors\u003c/h2\u003e\n \u003cp\u003eWith focusing on Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and table no. 5 we can realize that the document related to Nyholm\u0026amp; Smids (2016) with 151 citations is the first, and following it the document related to Hevelke\u0026amp; Nida-Rumelin (2015) with 133 citations, and the document of Gogoll\u0026amp; Mueller (2017) with 107 citations are the second and third respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cem\u003eTable No. 5 Top 10 References\u003c/em\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNyholm\u0026amp; Smids (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHevelke\u0026amp; Nida-Rumelin (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGogoll\u0026amp; Mueller (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDennis et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuglielmo et al. (2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsaro (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNyholm (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTorrance (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChen et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSparrow (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFurther, Nyhol, Sven is the first active author according to both the number of publications and citations. Following them, Smids, Jilles is the second according to citations with 2 publications and Nida- Rumelin, Julian has 133 citations (rank 3) with just 1 publication.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cem\u003eTable No. 6 Top 10 Authors\u003c/em\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNyhol, Sven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmids, Jilles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNida- Rumelin, Julian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGogoll, Jan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMueller, Julian F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlavkovik, Marija\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDennis, Louise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFisher, Michael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebster, Matt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuglielmo, Steve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eCo-Occurrence Map Based on Total Link Strength\u003c/h3\u003e\n\u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e the network between author in this field is very limited and it is divided into two groups. Chen, Long, Wng, Fei- Yue, and Wang, Fei- Yue with 2 number of documents and 66 citations have 21 link strength. Authors from rank 4th to 16th have 15 link strength and Poszler, Franziska in the 17th place has 3 publications with 26 citations and 10 link strength. Following them, Blind, Felix, Dittmer, Anke, and Faulhaber, Anja K have 1 document with 55 citations and 8 link strength.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cem\u003eTable No. 7 Top 20 link strength\u003c/em\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Link Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChen, Long\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang, Fei- Yue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCao, Dongpu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHu, Zhongxu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuang, Chao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi, Bai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi, Li\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi, Yuchen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi, Zixuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLv, Chen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa, Xiaoxiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeng, Siyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTian, Daxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang, Jinjun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXing, Yang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZheng, Nanning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoszler, Franziska\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlind, Felix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDittmer, Anke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaulhaber, Anja K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eThe Most 180 Days Usage\u003c/h3\u003e\n\u003cp\u003eThe document by Chen et al. 2023 with 61 TC has the most usage in 180 days with 132. Following them the document by Gebru et al. 2022 with 12 TC is the second place with number of 44. Other 13 documents are between 10 to 24.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cem\u003eTable No. 8 Top 20 180 Days Usage Counts\u003c/em\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabh\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKeywords\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e180 days usage counts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous vehicles; Planning; Vehicle dynamics; Heuristic algorithms; Object detection; Location awareness; Task analysis; Survey of surveys; milestones; autonomous driving; intelligent vehicles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIEEE TRANSACTIONS ON INTELLIGENT VEHICLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChen et al. 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticle measurements; Atmospheric measurements; CalibrationTask analysis; Reliability; Ethics; Automation; Human-machine trust; trust measurement; trust calibration; machine trustworthiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGebru et al. 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlockchain connected and autonomousvehicles; cybersecurity; deep learning; federated learning; internet of vehicles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAhmad et al. 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrolley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNATURE MACHINE INTELLIGENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeisslinger et al. 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous Vehicles; Trolley Problem; Self-driving cars; Ethical naturalism; Responsibility; Policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMINDS AND MACHINES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArfini et al. 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial intelligence; Automation; Engineering ethics; Self-driving cars; Socio-technical systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCIENCE AND ENGINEERING ETHICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorenstein et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData; Bias; Ethics; Artificial Intelligence; Machine Translation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHICS AND INFORMATION TECHNOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTomalin et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptance model; cybersecurity; human-centered artificial intelligence (HAI); human-robot interaction (HRI); performance of robots and autonomous system (RAS); safety; system health; trustiness; trustworthiness of RAS; worthiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe et al. 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoral Responsibility; Robots; ATTRIBUTABILITY; ACCOUNTABILITY; ANSWERABILITY; RETRIBUTION; Respect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHICS AND INFORMATION TECHNOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u0026ouml;nigs 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision-making; making process; Responsible AI; Intelligence cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHICS AND INFORMATION TECHNOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeerveld et al. 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics; Ethical; Moral; Autonomous car; Self-driving car; Responsibility; Liability; Duty to intervene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCIENCE AND ENGINEERING ETHICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHevelke\u0026amp; Nida-R\u0026uuml;melin, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous driving; Automation; Ethics; Morality; Dilemma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCIENCE AND ENGINEERING ETHICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGogoll\u0026amp; M\u0026uuml;ller, 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-driving; Autonomous vehicles; Automated vehicles; Ethics; Morals; Value systems; Artificial intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAI \u0026amp; SOCIETY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiegel\u0026amp; Pappas, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial intelligence; Machine ethics; AI alignment; Autonomous AI; Trustworthiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHICS AND INFORMATION TECHNOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirt, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous systems; Meaningful human control; Security; Decision-making; Human-Machine-Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHICS AND INFORMATION TECHNOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChristen et al. 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCo-Occurrence Map Based on Keywords\u003c/h2\u003e\n \u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and also table no. 9, the most occurrence term is \u0026quot;car\u0026quot; with relevance score of 0.86. the term of \u0026quot; Self \u0026quot; with the highest relevance score (0.95) was the second with 72 occurrences. Following them, the terms of \u0026quot;Aws\u0026quot; and \u0026quot;Argument\u0026quot; are in the fourth and fifth places with the relevance score of 62 and 57 respectively.\u003c/p\u003e\n \u003cp\u003eFurthermore, it can be seen that from fig no. 9 and table no. 10 the keywords of \u0026quot;Ethics\u0026quot; and \u0026quot;Artificial Intelligence\u0026quot; are the most relevant keywords in this field, with the occurrences of 44 and 31 respectively. In addition, these two keywords have 144 and 121 link strength respectively. Following them, the keywords of \u0026quot;Autonomous Vehicles\u0026quot; and \u0026quot;Responsibility\u0026quot; with 28 and 23 occurrences and 93 and 82 link strength respectively are the most used by researchers in these related fields. Interestingly, both \u0026quot;Robots\u0026quot; and \u0026quot;Self- driving cars\u0026quot; 17 times occurred in the researches.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cem\u003eTable No. 9 Top Terms According to Relevance Score\u003c/em\u003e\u003c/div\u003e\n \u003ctable id=\"Tabi\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurrences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelevance Score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAws\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArgument\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFramework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cem\u003eTable No. 10 Top Keywords\u003c/em\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabj\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKeyword\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurrences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Link Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous Vehicles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRobots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf- driving cars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrolley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutomation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current review included four steps 1. Literature retrieval; 2. Literature screening; 3. Content analysis; 4. Bibliometric analysis. In the first stage, we obtained 876 scientific documents using the Web of Science database with using related keywords. In the next step, we limited the found scientific documents to the years 2005 to 2023 and also limited this search only to documents in English. In addition, we only selected scientific documents in the categories of philosophy, ethics, computer science, and artificial intelligence, and after applying these limitations, we received 200 scientific documents for the review. In the next stage, we selected 107 articles for this review. The reason for choosing these 107 articles was that each document should have been cited at least 5 times. In the fourth stage, which is the systematic review stage, we investigated the number of documents, citations, and link strength based on countries, journals, documents, authors, and keywords using the VOSviewer (version 1.6.20). Further, we investigated active publishers in this field and documents that were used the most in the last 180 days. In this field, the most scientific documents are related to the United States of America, and the highest number of citations are related to the England. In relation to journals, the highest number of scientific documents is related to \u003cem\u003eEthics and Information Technology\u003c/em\u003e and the highest number of citations is related to \u003cem\u003eScience and Engineering Ethics\u003c/em\u003e. In this regard, the highest impact factor is related to the \u003cem\u003eArtificial Intelligence\u003c/em\u003e, which is published by Elsevier. However, Springer Publications has the most active journals in this field. In addition, the highest scientific document in this field has 151 citations and also the most active author in this field has 6 scientific documents and 287 citations. The document that have been used the most in the last 180 days are related to \u003cem\u003eIEEE Transactions on Intelligent Vehicles\u003c/em\u003e. On the other hand, the terms \"car\" and \"self\" have the most occurrences, and the keywords \"ethics\" and \"artificial intelligence\" also have the most occurrences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, J., Zia, M. U., Naqvi, I. H., Chattha, J. N., Butt, F. A., Huang, T., \u0026amp; Xiang, W. (2024). Machine learning and blockchain technologies for cybersecurity in connected vehicles. \u003cem\u003eWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery\u003c/em\u003e, 14(1), e1515.\u003c/li\u003e\n\u003cli\u003eArfini, S., Spinelli, D. \u0026amp; Chiffi, D. (2022). 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Artificial intelligence and moral dilemmas: Perception of ethical decision-making in AI. \u003cem\u003eJournal of Experimental Social Psychology\u003c/em\u003e, 101, 104327.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Impact Factor\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Times Cited\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ethics, Artificial Intelligence, AI, Autonomous Vehicles, VOSviewer.","lastPublishedDoi":"10.21203/rs.3.rs-5442122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5442122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: With the expansion of Artificial Intelligence (AI) in the contemporary era and the emergence of autonomous vehicles as a result, different ethical challenges have also arisen. Further, these challenges can be answered and investigated with different ethical and moral approaches. Therefore, we will find that this is a significant issue and also reviewing the researches that have been done in this regard is also of great importance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Using the four-steps method to conduct a systematic review, we first extracted related documents by searching for relevant keywords in the Web of Science (WoS) databases, and also conducted a systematic review using the VOSviewer (version 1.6.20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e After extracting these documents and using the VOSviewer, active countries in this field have been examined in terms of the number of documents and citations, active journals, active publishers, documents in terms of the number of citations, and also active authors in this field, as well as keywords and terms.\u003c/p\u003e","manuscriptTitle":"A Systematic Review About Moral Implications in Autonomous Vehicles Between 2005 and 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-14 10:32:31","doi":"10.21203/rs.3.rs-5442122/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38b0fb9d-ae5c-4286-96d4-ea6cb03d0b2f","owner":[],"postedDate":"November 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40175795,"name":"Artificial Intelligence and Machine Learning"},{"id":40175796,"name":"Philosophy"}],"tags":[],"updatedAt":"2024-11-14T10:32:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-14 10:32:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5442122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5442122","identity":"rs-5442122","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}