Biases in Artificial Intelligence and Implications for AI Use in Public Administration: A Technical Perspective

Biases in Artificial Intelligence and Implications for AI Use in Public Administration: A Technical Perspective | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 October 2025 V1 Latest version Share on Biases in Artificial Intelligence and Implications for AI Use in Public Administration: A Technical Perspective Author : Saban Contandriopoulos 0009-0008-9159-9712 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175934542.25288109/v1 213 views 110 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This article examines the technical foundations of artificial intelligence (AI) and their societal implications in the public sector. Beginning with an accessible presentation of AI mathematics and large language models (LLMs), it shows how biases, inherited from training data or model design, become embedded in model architectures. While AI performs well in tasks with clear outcomes, applying it in contexts requiring fairness or cultural sensitivity risks amplifying inequities. The article discusses the limits of bias-mitigation tools and cautions against generic models in high-stakes domains, underscoring the need for transparency, oversight, and context-sensitive design. Supplementary Material File (artificial intelligence - authorea.pdf) Download 147.58 KB Information & Authors Information Version history V1 Version 1 01 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords algorithmic bias artificial intelligence ethical ai government large language models neural networks Authors Affiliations Saban Contandriopoulos 0009-0008-9159-9712 [email protected] (Mathematics, University of Victoria View all articles by this author Metrics & Citations Metrics Article Usage 213 views 110 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Saban Contandriopoulos. Biases in Artificial Intelligence and Implications for AI Use in Public Administration: A Technical Perspective. Authorea . 01 October 2025. DOI: https://doi.org/10.22541/au.175934542.25288109/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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