{"id":37,"date":"2024-10-22T13:16:23","date_gmt":"2024-10-22T12:16:23","guid":{"rendered":"https:\/\/lifeofdata.org\/site\/algobias-toolkit\/?page_id=37"},"modified":"2025-10-09T12:05:08","modified_gmt":"2025-10-09T11:05:08","slug":"defining-fairness","status":"publish","type":"page","link":"https:\/\/lifeofdata.org\/site\/algobias-toolkit\/defining-fairness\/","title":{"rendered":"Defining &#8216;fairness&#8217;"},"content":{"rendered":"\n<figure class=\"wp-embed-aspect-16-9 wp-has-aspect-ratio wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"video-wrapper\"><iframe loading=\"lazy\" title=\"AlgoBias Toolkit: Defining fairness\" width=\"1300\" height=\"731\" src=\"https:\/\/www.youtube.com\/embed\/LhiOl1lctWI?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/div>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">What do we mean by \u2018fairness\u2019?<\/h2>\n\n\n\n<p class=\"\">Despite being widely used, the term does not have a single, universal definition. While in everyday conversation the word \u2018fair\u2019 is often used to describe something that is just, honest, and free from bias, it became a lot harder to define in the case of algorithmic technologies. Additionally, what\u2019s considered \u2018fair\u2019 can depend heavily on context, cultural values, individual beliefs, and the structures of power within a society.<\/p>\n\n\n\n<p class=\"\">When we discuss fairness in relation to systems &#8211; whether those systems are legal, social, algorithmic, or institutional &#8211; it often points to concerns about how resources, opportunities, risks, or outcomes are distributed, and whether those distributions are justifiable. There are many ways to understand fairness. Some of the most commonly used frameworks include:<\/p>\n\n\n\n<p class=\"\"><strong>Statistical fairness<\/strong>, which relies on producing metrics, such as accuracy scores and comparative statistics, to allow for comparison between different groups, to ensure treatment of all groups has been equal.<\/p>\n\n\n\n<p class=\"\"><strong>Procedural fairness<\/strong>, which is concerned this is fairness by way of process \u2013 the procedure for handling data is deemed fair, because the procedure stays the same for all groups. This could involve all cases being handled in the same way, or going through the same process, even if there might be mitigating factors. This refers to whether the process leading to a decision is fair \u2014 for example, whether individuals have the opportunity to be heard, whether rules are applied consistently, or whether decisions are made without bias.<\/p>\n\n\n\n<p class=\"\"><strong>Legal fairness,<\/strong> which is concerned about whether something would be deemed fair by legal structures and standards. While this might seem fairly straight forward, it can be more complicated in practice. A lot of legal structures focus on what what\u2019s \u2018reasonable\u2019, and this might not be clear to someone without any form of legal training. Often, what counts as reasonable is decided in case-law, but until the issue is brought to court, it might remain unclear.<\/p>\n\n\n\n<p class=\"\">And<strong> social justice-based conceptions of fairness<\/strong>, which focus less on the data or algorithm in question, but instead the context in which these technologies are produced in, and whether they exacerbate or assist in amending issues of inequality in the social system they are deployed within. This is also backed up by recent research, where it\u2019s been found members of the public were less concerned about the technology itself, but more with how the technology might exacerbate already existing problems found in the public sector (Ditchfield et al., 2022).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How might this impact algorithmic systems?<\/h2>\n\n\n\n<p class=\"\">Issues of fairness can become especially complex when decisions are made or shaped by algorithmic technologies or other types of data driven decision making.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Fairness in Algorithmic and Data-Driven Systems<\/h3>\n\n\n\n<p class=\"\">Fairness can be especially complex when decisions are made or shaped by algorithmic technologies. For example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"\">A hiring algorithm might be programmed to treat all applicants \u2018equally\u2019, but if it is trained on past hiring data that reflect existing biases, it may still produce unfair outcomes.<\/li>\n\n\n\n<li class=\"\">A credit scoring system might prioritise accuracy, but if it consistently disadvantages certain groups due to underlying socioeconomic inequalities, its fairness can be questioned.<\/li>\n\n\n\n<li class=\"\">A predictive policing tool may be designed to identify \u2018high risk\u2019 areas, but if based on historically biased arrest data, it can reinforce harmful patterns.<\/li>\n<\/ul>\n\n\n\n<p class=\"\">In these cases, fairness needs to be actively designed and assessed, not assumed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Reflective Questions for Assessing Fairness<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"\">What definition of fairness is being used in this situation?<\/li>\n\n\n\n<li class=\"\">Who benefits from this version of fairness? Who might be disadvantaged?<\/li>\n\n\n\n<li class=\"\">What assumptions are built into this definition?<\/li>\n\n\n\n<li class=\"\">How would someone from a different background or role view the fairness of this process or outcome?<\/li>\n\n\n\n<li class=\"\">Is the goal to correct past injustices, ensure equal treatment, or achieve equal outcomes?<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Further Resources<\/h3>\n\n\n\n<p class=\"\">Friedman, B. and Nissenbaum, H. (1996) \u2018Bias in Computer Systems\u2019, <em>ACM Transactions on Information Systems<\/em>, 14(3), pp. 330\u2013347. Available at: <a href=\"https:\/\/doi.org\/10.1145\/230538.230561\">doi.org\/10.1145\/230538.230561<\/a>.<\/p>\n\n\n\n<p class=\"\">Dencik, L. <em>et al. <\/em>(2022) <em>Data Justice<\/em>. London, UNITED KINGDOM: SAGE Publications, Limited. Available at: http:\/\/ebookcentral.proquest.com\/lib\/sheffield\/detail.action?docID=7121001<\/p>\n\n\n\n<p class=\"\">Costanza-Chock, S. (2020) <em>Design Justice<\/em>.<\/p>\n\n\n\n<p class=\"\">Balayn, A. and G\u00fcrses, S. (2021) <em>Beyond de-biasing: Regulating AI and its inequalities<\/em>. Belgium: European Digital Rights (ESRi). Available at: https:\/\/edri.org\/our-work\/if-ai-is-the-problem-is-debiasing-the-solution\/.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What do we mean by \u2018fairness\u2019? Despite being widely used, the term does not have a single, universal definition. While in everyday conversation the word \u2018fair\u2019 is often used to [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"nf_dc_page":"","footnotes":""},"class_list":["post-37","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Defining &#039;fairness&#039; - AlgoBias ToolKit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lifeofdata.org\/site\/algobias-toolkit\/defining-fairness\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Defining &#039;fairness&#039; - AlgoBias ToolKit\" \/>\n<meta property=\"og:description\" content=\"What do we mean by \u2018fairness\u2019? 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