A survey on bias in machine learning research

A survey on bias in machine learning research | 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 Research Article A survey on bias in machine learning research Agnieszka Mikołajczyk-Bareła, Michał Grochowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5019524/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 Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias. Bias was originally defined as a ”system-atic error” often caused by humans at different stages of the research process. This paper aims to bridge the gap between past and present literature on bias in research by providing taxonomy for potential sources of bias and errors in data and models, with special focus paid on bias in machine learning pipelines. Survey analyses over forty potential sources of bias in the machine learning (ML) pipeline, providing clear examples for each. By understanding the sources and consequences of bias in machine learning, better methods can be developed for its detection and mitigation, which lead to fairer, more transparent, and more accurate ML models. bias fairness machine learning Full Text Additional Declarations Competing interest reported. The authors declare the following competing interests: Gdańsk University of Technology, Voicelab.ai, Chaptr.ai. 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-5019524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359440998,"identity":"cc923867-669e-4674-925f-85fc8971baf6","order_by":0,"name":"Agnieszka Mikołajczyk-Bareła","email":"data:image/png;base64,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","orcid":"","institution":"Gdańsk University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Agnieszka","middleName":"","lastName":"Mikołajczyk-Bareła","suffix":""},{"id":359440999,"identity":"39735b74-12cd-4286-ae0c-fde75526689a","order_by":1,"name":"Michał Grochowski","email":"","orcid":"","institution":"Gdańsk University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Michał","middleName":"","lastName":"Grochowski","suffix":""}],"badges":[],"createdAt":"2024-09-02 15:29:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5019524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5019524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69046750,"identity":"8ceb6beb-008b-4093-a2df-651dc8aa4c3d","added_by":"auto","created_at":"2024-11-15 03:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1203731,"visible":true,"origin":"","legend":"","description":"","filename":"AsurveyonbiasinmachinelearningAIreview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5019524/v1_covered_69ab1c8e-7776-42a0-b2d8-36f1cb475e27.pdf"}],"financialInterests":"Competing interest reported. 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