{"id":72541,"date":"2024-04-19T00:12:42","date_gmt":"2024-04-19T04:12:42","guid":{"rendered":"https:\/\/world.edu\/?p=72541"},"modified":"2024-04-19T00:12:42","modified_gmt":"2024-04-19T04:12:42","slug":"understanding-ai-outputs-study-shows-pro-western-cultural-bias-in-the-way-ai-decisions-are-explained","status":"publish","type":"post","link":"https:\/\/world.edu\/understanding-ai-outputs-study-shows-pro-western-cultural-bias-in-the-way-ai-decisions-are-explained\/","title":{"rendered":"Understanding AI outputs: study shows pro-western cultural bias in the way AI decisions are explained"},"content":{"rendered":"

\"\"<\/p>\n

Humans are increasingly using artificial intelligence (AI) to inform decisions about our lives. AI is, for instance, helping to\u00a0make hiring choices<\/a>\u00a0and\u00a0offer medical diagnoses<\/a>.<\/p>\n

If you were affected, you might want an explanation of why an AI system produced the decision it did. Yet AI systems are often so computationally complex that\u00a0not even their designers fully know<\/a>\u00a0how the decisions were produced. That\u2019s why the development of \u201cexplainable AI\u201d (or XAI)\u00a0is booming<\/a>. Explainable AI includes systems that are either themselves simple enough to be fully understood by people, or that produce easily understandable explanations of other, more complex AI models\u2019\u00a0outputs<\/a>.<\/p>\n

Explainable AI systems help AI engineers to\u00a0monitor and correct their models\u2019 processing<\/a>. They also help users to make informed decisions about whether to trust or how best to use AI outputs.<\/p>\n

Not all AI systems\u00a0need to be explainable<\/a>. But in high-stakes domains, we can expect XAI to become widespread. For instance, the recently adopted\u00a0European AI Act<\/a>, a forerunner for similar laws worldwide, protects a \u201cright to explanation\u201d. Citizens have a right to receive an explanation about an AI decision that affects their other rights.<\/p>\n

But what if something like your cultural background affects what explanations you expect from an AI?<\/p>\n

In\u00a0a recent systematic review<\/a>\u00a0we analysed over 200 studies from the last ten years (2012\u20132022) in which the explanations given by XAI systems were tested on people. We wanted to see to what extent researchers indicated awareness of cultural variations that were potentially relevant for designing satisfactory explainable AI.<\/p>\n

Our findings suggest that many existing systems may produce explanations that are primarily tailored to individualist, typically western, populations (for instance, people in the US or UK). Also, most XAI user studies only sampled western populations, but\u00a0unwarranted generalisations<\/a>\u00a0of results to non-western populations were pervasive.<\/p>\n

Cultural differences in explanations<\/strong><\/h4>\n

There are two common ways to explain someone\u2019s actions. One involves invoking the person\u2019s beliefs and desires. This explanation is internalist, focused on what\u2019s going on inside someone\u2019s head. The other is externalist, citing factors like social norms, rules, or other factors that are outside the person.<\/p>\n

To see the difference, think about how we might explain a driver\u2019s stopping at a red traffic light. We could say, \u201cThey believe that the light is red and don\u2019t want to violate any traffic rules, so they decided to stop.\u201d This is an internalist explanation. But we could also say, \u201cThe lights are red and the traffic rules require that drivers stop at red lights, so the driver stopped.\u201d This is an externalist explanation.<\/p>\n

Many\u00a0psychological<\/a>\u00a0studies suggest internalist explanations are preferred in \u201cindividualistic\u201d countries where people often view themselves as more independent from others.\u00a0These countries<\/a>\u00a0tend to be in the west, educated, industrialised, rich, and democratic.<\/p>\n

However, such explanations are not obviously preferred over externalist explanations in \u201ccollectivist\u201d societies, such as those commonly found across Africa or south Asia, where people often view themselves as interdependent.<\/p>\n

Preferences in explaining behaviour are relevant for what a successful XAI output could be. An AI that offers a medical diagnosis might be accompanied by an explanation such as: \u201cSince your symptoms are fever, sore throat and headache, the classifier\u00a0thinks<\/em>\u00a0you have flu.\u201d This is internalist because the explanation invokes an \u201cinternal\u201d state of the AI \u2013 what it \u201cthinks\u201d \u2013 albeit metaphorically. Alternatively, the diagnosis could be accompanied by an explanation that does not mention an internal state, such as: \u201cSince your symptoms are fever, sore throat and headache,\u00a0based on its training on diagnostic inclusion criteria<\/em>, the classifier produces the output that you have flu.\u201d This is externalist. The explanation draws on \u201cexternal\u201d factors like inclusion criteria, similar to how we might explain stopping at a traffic light by appealing to the rules of the road.<\/p>\n

If people from different cultures prefer different kinds of explanations, this matters for designing inclusive systems of explainable AI.<\/p>\n

Our research, however, suggests that XAI developers are not sensitive to potential cultural differences in explanation preferences.<\/p>\n

Overlooking cultural differences<\/strong><\/h4>\n

A striking 93.7% of the studies we reviewed did not indicate awareness of cultural variations potentially relevant to designing explainable AI. Moreover, when we checked the cultural background of the people tested in the studies, we found 48.1% of the studies did not report on cultural background at all. This suggests that researchers did not consider cultural background to be a factor that could influence the generalisability of results.<\/p>\n

Of those that did report on cultural background, 81.3% only sampled western, industrialised, educated, rich and democratic populations. A mere 8.4% sampled non-western populations and 10.3% sampled mixed populations.<\/p>\n

Sampling only one kind of population need not be a problem if conclusions are limited to that population, or researchers give reasons to think other populations are similar. Yet, out of the studies that reported on cultural background, 70.1% extended their conclusions beyond the study population \u2013 to users, people, humans in general \u2013 and most studies did not contain evidence of reflection on cultural similarity.<\/p>\n

To see how deep the oversight of culture runs in explainable AI research, we added a systematic \u201cmeta\u201d review of 34 existing literature reviews of the field. Surprisingly, only two reviews commented on western-skewed sampling in user research, and only one review mentioned overgeneralisations of XAI study findings.<\/p>\n

This is problematic.<\/p>\n

Why the results matter<\/strong><\/h4>\n

If findings about explainable AI systems only hold for one kind of population, these systems may not meet the explanatory requirements of other people affected by or using them. This can diminish trust in AI. When AI systems make high-stakes decisions but don\u2019t give you a satisfactory explanation, you\u2019ll likely distrust them even if their decisions (such as medical diagnoses) are accurate and important for you.<\/p>\n

To address this cultural bias in XAI, developers and psychologists should collaborate to test for relevant cultural differences. We also recommend that cultural backgrounds of samples be reported with XAI user study findings.<\/p>\n

Researchers should\u00a0state<\/a>\u00a0whether their study sample represents a wider population. They may also use\u00a0qualifiers<\/a>\u00a0like \u201cUS users\u201d or \u201cwestern participants\u201d in reporting their findings.<\/p>\n

As AI is being used worldwide to make important decisions, systems must provide explanations that people from different cultures find acceptable. As it stands, large populations who could benefit from the potential of explainable AI risk being overlooked in XAI research.<\/p>\n

Author Bios:<\/strong> Mary Carman is a Senior Lecturer in Philosophy at the University of the Witwatersrand and Uwe Peters is Assistant Professor of Philosophy at Utrecht University<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Humans are increasingly using artificial intelligence (AI) to inform decisions about our lives. AI is, for instance, helping to\u00a0make hiring choices\u00a0and\u00a0offer medical diagnoses. If you were affected, you might want an explanation of why an AI system produced the decision it did. Yet AI systems are often so computationally complex that\u00a0not even their designers fully […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[434919],"coauthors":[11733],"class_list":["post-72541","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-ai-data-outputs"],"acf":[],"yoast_head":"\nUnderstanding AI outputs: study shows pro-western cultural bias in the way AI decisions are explained - World leading higher education information and services<\/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:\/\/world.edu\/understanding-ai-outputs-study-shows-pro-western-cultural-bias-in-the-way-ai-decisions-are-explained\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"Understanding AI outputs: study shows pro-western cultural bias in the way AI decisions are explained - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"Humans are increasingly using artificial intelligence (AI) to inform decisions about our lives. 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