{"id":32785,"date":"2026-06-18T20:57:50","date_gmt":"2026-06-18T10:57:50","guid":{"rendered":"https:\/\/www.chillicomment.com\/?p=32785"},"modified":"2026-06-18T20:57:50","modified_gmt":"2026-06-18T10:57:50","slug":"interactive-why-do-ai-models-struggle-with-online-hate-speech-detection","status":"publish","type":"post","link":"https:\/\/www.chillicomment.com\/?p=32785&lang=en","title":{"rendered":"Interactive Why do AI models struggle with online hate speech detection?"},"content":{"rendered":"<div class=\"article-info m--t-20 m--desktop-b-30 m--b-mobile-20\">\n<div class=\"byline byline--multiple-avatars\">\n<div class=\"byline-content\">\n<div class=\"contributors-list contributors-list--byline\"><span class=\"contributors-list__by-prefix\">By\u00a0<\/span>Hanna Duggal\u00a0and\u00a0Mohammed Haddad<\/div>\n<\/div>\n<\/div>\n<div class=\"article-dates\">\n<div class=\"date-simple\"><span class=\"screen-reader-text\">Published On 18 Jun 2026<\/span><\/div>\n<\/div>\n<\/div>\n<div class=\"wysiwyg wysiwyg--all-content\" aria-live=\"polite\" aria-atomic=\"true\">\n<p>Hate speech that once circulated in person now travels farther and faster via anonymous online accounts behind a screen.<\/p>\n<p>As the United Nations marks the\u00a0International Day\u00a0for Countering Hate Speech on June 18, UN Secretary-General Antonio Guterres has warned that social platforms are amplifying the threat.<\/p>\n<p>With artificial intelligence (AI) increasingly tasked with detecting and removing hate speech online, Al Jazeera looks at where these systems fall short compared with human judgement.<\/p>\n<h2 id=\"how-is-hate-speech-defined\">How is hate speech defined?<\/h2>\n<p>According to the UN, hate speech covers any communication \u2013 spoken, written or behavioural \u2013 that discriminates against or incites violence towards a person or group.<\/p>\n<p>The UN states that hate speech targets a person\u2019s actual or perceived identity, race, ethnicity, religion, gender, sexual orientation or disability. And it isn\u2019t limited to words, with the UN noting it can also take the form of images, cartoons, gestures and even objects.<\/p>\n<h2 id=\"how-many-people-encounter-hate-speech-online\">How many people encounter hate speech online?<\/h2>\n<p>According to a\u00a02023\u00a0joint survey of 8,000 people in 16 countries done by polling company Ipsos and the UN Educational, Scientific and Cultural Organization (UNESCO), more than two-thirds of internet users encountered hate speech online.<\/p>\n<p>The survey also found that 33 percent of people thought LGBTQI people experienced the most cases of hate speech, followed by ethnic and racial minorities (28 percent) and women (18 percent).<\/p>\n<p>Meta, which owns Facebook, has removed fewer hateful posts since 2023. In the last quarter of 2025, the company removed 1.3 million posts from Instagram and 1.3 million from Facebook, compared to 7.4 million removed from Instagram and 5.8 million from Facebook in the fourth quarter of 2024.<\/p>\n<p>This came as the company shifted away from proactive detection of hate speech and relied more on users to report encounters.<\/p>\n<p>On the other hand, TikTok\u00a0said\u00a0it removed 96.3 percent of all hate speech and content in the fourth quarter of 2025 before it was reported.<\/p>\n<div class=\"nudge\" data-testid=\"nudge\"><span class=\"nudge__text\">Get instant alerts and updates based on your interests. Be the first to know when big stories happen.<\/span><\/div>\n<p>To detect and combat the spread of hate speech online, social media companies have increasingly turned to AI, using content moderation systems powered by large language models (LLMs) that promise to automate content filtering across huge volumes of messages.<\/p>\n<p>In general, these systems use labeled datasets and pretrained language models to detect abusive language. They then apply rules or score thresholds to decide whether content is hateful or violates company policies.<\/p>\n<p>A 2025\u00a0study\u00a0by researchers at the University of Pennsylvania found that these models vary widely in how they identify and classify hate speech, with significant inconsistencies across systems and demographic groups, raising concerns about bias and unequal protection online.<\/p>\n<p>The study evaluated seven AI moderation systems \u2013 including models from OpenAI, Anthropic, DeepSeek, Mistral, and Google \u2013 and found major differences in how they identified and scored hate speech across categories.<\/p>\n<p>This chart shows how different AI moderation systems scored the severity of hate speech targeting the same groups on a 0\u20131 scale. Higher values indicate the model judged the content as more hateful.<\/p>\n<p>Mistral Moderation Endpoint is often clustered very close to 1, meaning it labels many examples as highly hateful regardless of the target group.<\/p>\n<p>OpenAI Moderation Endpoint tends to produce much lower scores for many categories, sometimes less than half the score assigned by other models.<\/p>\n<p>As the study authors put it, \u201cIf two systems produce different outcomes for the same piece of content \u2013 flagging it as hate speech in one case but not in another \u2013 it undermines the legitimacy of the moderation process.\u201d<\/p>\n<h2 id=\"the-limitations-of-ai-hate-speech-detection\">The limitations of AI hate speech detection<\/h2>\n<p>While AI systems are able to detect explicit hate speech \u2013 for example, when profanities and slurs are used against a particular group \u2013 more nuanced examples are missed by LLMs.<\/p>\n<p>\u201cOne challenging example is the case of implicit hate speech, which is often not detected as such because it contains no mention of slurs,\u201d Arkaitz Zubiaga, an associate professor at Queen Mary University of London, and co-lead of the university\u2019s Social Data Science lab, told Al Jazeera. \u201cThis could be the case of a positive-sounding message such as \u201cI would love to see how great the world would be if\u2026\u201d followed by a derogatory message disparaging a demographic group. AI systems can struggle to see the hate in those messages if they focus instead on the positive side of the message.\u201d<\/p>\n<p>Zubiaga adds that the opposite is also true, where seemingly offensive words, which are now incorporated into language for more endearing purposes, are highlighted as hate speech.<\/p>\n<p>\u201cThis is the case of reclaimed language, where keywords that are historically deemed slurs are embraced and repurposed by the communities they were initially used to disparage, and the slurs are then used between members of the marginalised community,\u201d he said. \u201cWhile these cases should not be flagged as hateful, AI systems have a tendency to do it.\u201d<\/p>\n<p>(ALJAZEERA)<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>By\u00a0Hanna Duggal\u00a0and\u00a0Mohammed Haddad Published On 18 Jun [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":30776,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[58],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Interactive Why do AI models struggle with online hate speech detection? 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