{"id":22877,"date":"2024-07-24T18:15:17","date_gmt":"2024-07-24T12:45:17","guid":{"rendered":"https:\/\/triumphias.com\/blog\/?p=22877"},"modified":"2024-07-24T18:16:48","modified_gmt":"2024-07-24T12:46:48","slug":"algorithmic-biasness","status":"publish","type":"post","link":"https:\/\/triumphias.com\/blog\/algorithmic-biasness\/","title":{"rendered":"Algorithmic Biasness | Ethics for UPSC Civil Services Examination | Triumph IAS"},"content":{"rendered":"<h1 style=\"text-align: center;\"><span style=\"font-family: georgia, palatino, serif;\"><strong><span style=\"color: #ff0000;\"><b><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-22780\" src=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/A-Comprehensive-Classroom-cum-Test-Series-1-150x58.png\" alt=\"\" width=\"986\" height=\"381\" srcset=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/A-Comprehensive-Classroom-cum-Test-Series-1-150x58.png 150w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/A-Comprehensive-Classroom-cum-Test-Series-1-300x115.png 300w\" sizes=\"auto, (max-width: 986px) 100vw, 986px\" \/><br \/>\n<\/b><\/span><\/strong><\/span><\/h1>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_68 ez-toc-wrap-center counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >What's Inside this Blog!<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/triumphias.com\/blog\/algorithmic-biasness\/#Algorithmic_Biasness\" title=\"Algorithmic Biasness\">Algorithmic Biasness<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/triumphias.com\/blog\/algorithmic-biasness\/#Relevant_for_Public_Ethics_Integrity_and_Aptitude\" title=\"[Relevant for Public Ethics, Integrity and Aptitude]\">[Relevant for Public Ethics, Integrity and Aptitude]<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/triumphias.com\/blog\/algorithmic-biasness\/#Related_Blogs_%E2%80%A6\" title=\"Related Blogs &#8230;\">Related Blogs &#8230;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/triumphias.com\/blog\/algorithmic-biasness\/#Find_More_Blogs%E2%80%A6\" title=\"Find More Blogs&#8230;\">Find More Blogs&#8230;<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1 style=\"text-align: center;\"><span class=\"ez-toc-section\" id=\"Algorithmic_Biasness\"><\/span><span style=\"font-family: georgia, palatino, serif; color: #ff0000;\"><strong>Algorithmic Biasness<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1 style=\"text-align: center;\"><span class=\"ez-toc-section\" id=\"Relevant_for_Public_Ethics_Integrity_and_Aptitude\"><\/span><span style=\"font-family: georgia, palatino, serif;\"><span style=\"font-size: 24px;\"><em>[Re<span style=\"font-size: 20px;\">levant for Public <\/span><\/em><\/span><span style=\"font-size: 20px;\"><em><strong style=\"font-weight: bold;\">Ethics, Integrity and Aptitude<\/strong><\/em><em><b>]<\/b><\/em><\/span><\/span><span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h3 style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Algorithmic Biasness <\/strong><\/span><\/h3>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">In the present era of Artificial Intelligence (AI) and Machine Learning (ML) AI becomes capable of analyzing data set and coming out with viable results for any query put to it. Through ML that AI keeps evolving over time and becomes more and more expert in answering questions.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-22878\" src=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/sd-150x84.png\" alt=\"\" width=\"805\" height=\"451\" srcset=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/sd-150x84.png 150w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/sd-300x169.png 300w\" sizes=\"auto, (max-width: 805px) 100vw, 805px\" \/><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">But, there is a certain \u201cdark spots\u201d in this process; what we call as Algorithmic Bias. \u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">This biasness has made increasingly apparent that the promises of AI aren\u2019t distributed equally \u2014 it risks exacerbating social and economic disparities, particularly across demographic characteristics such as race and in India towards certain denotified castes or may be certain biasness against rural people.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">Business and government leaders are being called on to ensure the benefits of AI-driven advancements are accessible to all. Yet it seems that for each passing day there is some new way in which AI creates inequality, resulting in a reactive patchwork of solutions \u2014 or often no response at all.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">Algorithmic bias occurs when algorithms make decisions that systematically disadvantage certain groups of people.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">But why do this happen in the first place?<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">If we take India into consideration and criminal (both convicted and suspected) data banks and make machine learn from the profiles of each data set; naturally machine learning would involve all biasness of the data sets to get into its learning. So, next time when one would put in a new data and inquire about its probability of criminal antecedent than depending upon the biased learning upon which that machine has learnt it would throw a bias result.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">To understand it simply: take for example \u2013 most undertrials and criminals come from a certain social background having certain educational backgrounds (mostly non-matriculate) so, a machine having learnt on that data-sets would always have that inbuilt bias to identify any new data set having similar socio-educational background to term it as criminal even if that person has done some small mistake like breaking red light. With time this biasness would grow in that algorithm thus making it bias against certain castes, locality or may be with certain educational background.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-22879\" src=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/Al-150x84.jpg\" alt=\"\" width=\"798\" height=\"447\" srcset=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/Al-150x84.jpg 150w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2024\/07\/Al-300x169.jpg 300w\" sizes=\"auto, (max-width: 798px) 100vw, 798px\" \/><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">Algorithmic bias often occurs because certain populations are underrepresented in the data used to train AI algorithms or because pre-existing societal prejudices are baked into the data itself.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">It can have disastrous consequences when applied to key areas such as healthcare, criminal justice, and credit scoring. Scientists investigating a widely used healthcare algorithm found that it severely underestimated the needs of rural women as far as screening for breast cancer was concerned, leading to significantly less care. This is not just unfair, but profoundly harmful.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">While minimizing algorithmic bias is an important piece of the puzzle, unfortunately it is not sufficient for ensuring equitable outcomes. Complex social processes and market forces lurk beneath the surface, giving rise to a landscape of winners and losers that cannot be explained by algorithmic bias alone. To fully understand this uneven landscape, we need to understand how AI shapes the supply and demand for goods and services in ways that perpetuate and even create inequality.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">Afterall did we forget that even after all this development in ML and AI; machines are still based on GIGO \u2013 Garbage In is what you get i.e. Garbage Out.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">It is thus important that before State gives too much discretion on such Algorithm vis-\u00e0-vis such important data points interpretation which involves issues related to civil liberties, privacy and other ethical dimensions of individual liberty it is important to make such ML learning based on neutral data set and if not possible than to make certain provisions in the algorithm itself where such biasness is minimized. But, the bigger question is \u2013 Is it possible?<\/span><\/p>\n<p style=\"text-align: justify;\"><em style=\"font-size: 16px; color: #ff0000; font-family: georgia, palatino, serif; text-align: left;\"><strong>(Reference: Static portion)<\/strong><\/em><\/p>\n<hr \/>\n<h2><span class=\"ez-toc-section\" id=\"Related_Blogs_%E2%80%A6\"><\/span><span style=\"font-family: georgia, palatino, serif;\">Related Blogs &#8230;<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table style=\"border-collapse: collapse; width: 100%;\">\n<tbody>\n<tr>\n<td style=\"width: 50%;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"https:\/\/triumphias.com\/blog\/ethical-standards-in-public-service\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-17836\" src=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-300x241.jpg\" alt=\"Ethical Standards in Public Service, Best Sociology Optional Coaching, Sociology Optional Syllabus.\" width=\"393\" height=\"316\" srcset=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-300x241.jpg 300w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-1024x824.jpg 1024w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-150x121.jpg 150w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-768x618.jpg 768w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-1536x1236.jpg 1536w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-93-2048x1648.jpg 2048w\" sizes=\"auto, (max-width: 393px) 100vw, 393px\" \/><\/a><\/span><\/td>\n<td style=\"width: 50%;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"https:\/\/triumphias.com\/blog\/integrity-pact-in-indias-public-procurement\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-16756\" src=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-300x241.jpg\" alt=\"Navigating the Complex Terrain of Integrity Pact in India's Public Procurement, Best Sociology Optional Coaching, Sociology Optional Syllabus.\" width=\"400\" height=\"321\" srcset=\"https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-300x241.jpg 300w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-1024x824.jpg 1024w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-150x121.jpg 150w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-768x618.jpg 768w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-1536x1236.jpg 1536w, https:\/\/triumphias.com\/blog\/wp-content\/uploads\/2023\/09\/Add-a-heading-23-2048x1648.jpg 2048w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<table style=\"border-collapse: collapse; width: 100%;\">\n<tbody>\n<tr>\n<td style=\"width: 50%;\"><iframe loading=\"lazy\" src=\"\/\/www.youtube.com\/embed\/kt3VeKYqoSs?t=54s\" width=\"350\" height=\"196\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td style=\"width: 50%;\"><span style=\"font-family: georgia, palatino, serif;\"><sup><iframe loading=\"lazy\" src=\"\/\/www.youtube.com\/embed\/dL85EHfcitw?t=17s\" width=\"350\" height=\"196\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/sup><\/span><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 50%;\"><iframe loading=\"lazy\" src=\"\/\/www.youtube.com\/embed\/tJcm9G2RwVE\" width=\"350\" height=\"196\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td style=\"width: 50%;\"><iframe loading=\"lazy\" src=\"\/\/www.youtube.com\/embed\/YXsRAVmvsCE?t=1s\" width=\"350\" height=\"196\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><iframe loading=\"lazy\" src=\"\/\/www.youtube.com\/embed\/p9IYaTuS3sA?si=CiiRvqWVd_sdZNlm\" width=\"750\" height=\"421\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\">Follow us :<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"https:\/\/www.instagram.com\/triumphias\/\" target=\"_blank\" rel=\"noopener\">\ud83d\udd0e https:\/\/www.instagram.com\/triumphias<\/a><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"\ud83d\udd0e www.triumphias.com\" target=\"_blank\" rel=\"noopener\">\ud83d\udd0e www.triumphias.com<\/a><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"https:\/\/www.youtube.com\/c\/TriumphIAS\" target=\"_blank\" rel=\"noopener\">\ud83d\udd0e https:\/\/www.youtube.com\/c\/TriumphIAS<\/a><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: georgia, palatino, serif;\"><a href=\"https:\/\/www.youtube.com\/c\/TriumphIAS\" target=\"_blank\" rel=\"noopener\">\ud83d\udd0e<\/a><a href=\"https:\/\/t.me\/VikashRanjanSociology\" target=\"_blank\" rel=\"noopener\"> https:\/\/t.me\/VikashRanjanSociology<\/a><\/span><\/p>\n<h2 style=\"text-align: justify;\"><span class=\"ez-toc-section\" id=\"Find_More_Blogs%E2%80%A6\"><\/span><span style=\"color: #808000; 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