The Colorblind Algorithm? Racial Bias in AI-Powered Skin Cancer Detection Models

The Colorblind Algorithm? Racial Bias in AI-Powered Skin Cancer Detection Models | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 8 December 2025 V1 Latest version Share on The Colorblind Algorithm? Racial Bias in AI-Powered Skin Cancer Detection Models Author : Ela Adhikari 0009-0004-6673-6050 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176523640.07809881/v1 366 views 187 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Artificial intelligence (AI) models are increasingly used to distinguish benign from malignant skin lesions. Despite their promise, recent evidence suggests that these models often perform unevenly across skin-tone and racial groups, raising concerns that technology intended to expand care could instead exacerbate health disparities. This review summarizes the existing evidence for racial bias, underlying causes, and solutions to mitigate inequities. Overall, the literature consistently reports that algorithms trained predominantly on light-skinned images underperform on darker skin. When models are retrained using representative datasets or synthetic images of dark skin, their performance improves. Addressing data diversity, transparent reporting and fairness auditing will be essential to ensure AI models truly benefit diverse populations. Supplementary Material File (the colorblind algorithm.pdf) Download 154.18 KB Information & Authors Information Version history V1 Version 1 08 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence dark skin health disparities racial bias skin cancer Authors Affiliations Ela Adhikari 0009-0004-6673-6050 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 366 views 187 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ela Adhikari. The Colorblind Algorithm? Racial Bias in AI-Powered Skin Cancer Detection Models. Authorea . 08 December 2025. DOI: https://doi.org/10.22541/au.176523640.07809881/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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