{"id":13337,"date":"2024-07-08T11:26:24","date_gmt":"2024-07-08T11:26:24","guid":{"rendered":"https:\/\/dailyai.com\/?p=13337"},"modified":"2024-07-08T11:26:24","modified_gmt":"2024-07-08T11:26:24","slug":"ai-lie-detector-beats-humans-and-could-be-socially-disruptive","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/07\/ai-lie-detector-beats-humans-and-could-be-socially-disruptive\/","title":{"rendered":"AI-l\u00f8gnedetektor sl\u00e5r mennesker og kan v\u00e6re socialt forstyrrende"},"content":{"rendered":"<p><strong>Forskere fra University of W\u00fcrzburg og Max-Planck Institute for Human Development har tr\u00e6net en AI-model til at opdage l\u00f8gne, og det kan \u00e6ndre den m\u00e5de, vi omg\u00e5s hinanden p\u00e5.<\/strong><\/p>\n<p>Mennesker er ikke gode til at se, om en person lyver eller taler sandt. Eksperimenter viser, at vores hitrate i bedste fald er omkring 50%, og denne d\u00e5rlige pr\u00e6station dikterer, hvordan vi interagerer med hinanden.<\/p>\n<p>The truth-default theory (TDT) siger, at folk typisk vil antage, at det, en person fort\u00e6ller dem, er sandt. De sociale omkostninger ved at kalde personen en l\u00f8gner er for stor en risiko med vores 50\/50 evne til at opdage l\u00f8gne, og faktatjek er ikke altid praktisk i \u00f8jeblikket.<\/p>\n<p>Polygrafer og anden l\u00f8gnedetekterende teknologi kan opfange data som stressindikatorer og \u00f8jenbev\u00e6gelser, men det er ikke sandsynligt, at du vil bruge en af disse i din n\u00e6ste samtale. Kan AI hj\u00e6lpe?<\/p>\n<p><a href=\"https:\/\/www.cell.com\/action\/showPdf?pii=S2589-0042%2824%2901426-3\" target=\"_blank\" rel=\"noopener\">Avisen<\/a> forklarer, hvordan forskerholdet tr\u00e6nede Googles BERT LLM til at opdage, n\u00e5r folk l\u00f8j.<\/p>\n<p>Forskerne rekrutterede 986 deltagere og bad dem om at beskrive deres weekendplaner med en opf\u00f8lgende forklaring, der underst\u00f8ttede sandhedsv\u00e6rdien af deres udsagn.<\/p>\n<p>Derefter blev de pr\u00e6senteret for en anden deltagers weekendplaner og bedt om at skrive en falsk st\u00f8tteerkl\u00e6ring, hvor de argumenterede for, at det faktisk var deres planer for weekenden.<\/p>\n<p>BERT blev tr\u00e6net p\u00e5 80% af de 1.536 udsagn og fik derefter til opgave at evaluere sandf\u00e6rdigheden af resten af udsagnene.<\/p>\n<p>Modellen var i stand til at m\u00e6rke et udsagn som sandt eller falsk med en n\u00f8jagtighed p\u00e5 66,86%, hvilket er betydeligt bedre end de menneskelige dommere, der opn\u00e5ede en n\u00f8jagtighed p\u00e5 46,47% i yderligere eksperimenter.<\/p>\n<h2>Ville du bruge en AI-l\u00f8gnedetektor?<\/h2>\n<p>Forskerne fandt ud af, at n\u00e5r deltagerne blev pr\u00e6senteret for muligheden for at bruge AI-l\u00f8gnedetekteringsmodellen, var det kun en tredjedel, der besluttede sig for at tage imod tilbuddet.<\/p>\n<p>De, der valgte at bruge algoritmen, fulgte n\u00e6sten altid den algoritmiske forudsigelse ved at acceptere udsagnet som sandt eller komme med en anklage om l\u00f8gn.<\/p>\n<p>Deltagere, der s\u00f8gte algoritmiske forudsigelser, viste beskyldningsrater p\u00e5 n\u00e6sten 85%, n\u00e5r det antydede, at udsagnet var falsk. Baseline for dem, der ikke bad om maskinelle forudsigelser, var 19,71%.<\/p>\n<p>Folk, der er \u00e5bne over for ideen om en AI-l\u00f8gnedetektor, er mere tilb\u00f8jelige til at kalde det BS, n\u00e5r de ser den r\u00f8de lampe blinke.<\/p>\n<p>Forskerne foresl\u00e5r, at \"En plausibel forklaring er, at en tilg\u00e6ngelig l\u00f8gndetekteringsalgoritme giver mulighed for at overf\u00f8re ansvaret for beskyldninger fra en selv til det maskinl\u00e6rende system.\"<\/p>\n<p>\"Det er ikke mig, der kalder dig en l\u00f8gner, det er maskinen.<\/p>\n<h2>Dette \u00e6ndrer alt<\/h2>\n<p>Hvad ville der ske i vores samfund, hvis folk var fire gange mere tilb\u00f8jelige til at kalde hinanden l\u00f8gnere?<\/p>\n<p>Forskerne konkluderede, at hvis folk stolede p\u00e5, at kunstig intelligens ville v\u00e6re sandhedens dommer, kunne det have et st\u00e6rkt forstyrrende potentiale.<\/p>\n<p>Avisen bem\u00e6rkede, at \"h\u00f8je beskyldningsrater kan belaste vores sociale struktur ved at fremme generel mistillid og yderligere \u00f8ge polariseringen mellem grupper, der allerede har sv\u00e6rt ved at stole p\u00e5 hinanden.\"<\/p>\n<p>En n\u00f8jagtig AI-l\u00f8gnedetektor ville ogs\u00e5 have positive konsekvenser. Den kunne identificere AI-genereret misinformation og falske nyheder, hj\u00e6lpe i forretningsforhandlinger eller bek\u00e6mpe forsikringssvindel.<\/p>\n<p>Hvad med etikken i at bruge et v\u00e6rkt\u00f8j som dette? Kunne gr\u00e6nseagenter bruge det til at opdage, om en migrants asylans\u00f8gning var sand eller et opportunistisk p\u00e5fund?<\/p>\n<p>Mere avancerede modeller end BERT vil sandsynligvis skubbe AI's l\u00f8gndetektering til et punkt, hvor menneskelige fors\u00f8g p\u00e5 bedrag bliver alt for lette at f\u00e5 \u00f8je p\u00e5.<\/p>\n<p>Forskerne konkluderede, at deres \"forskning understreger det presserende behov for en omfattende politisk ramme for at h\u00e5ndtere virkningen af AI-drevne l\u00f8gnedetekteringsalgoritmer.\"<\/p>","protected":false},"excerpt":{"rendered":"<p>Forskere fra University of W\u00fcrzburg og Max-Planck Institute for Human Development har tr\u00e6net en AI-model til at opdage l\u00f8gne, og det kan \u00e6ndre den m\u00e5de, vi omg\u00e5s hinanden p\u00e5. Mennesker er ikke gode til at se, om en person lyver eller taler sandt. Eksperimenter viser, at vores hitrate i bedste fald er omkring 50%, og denne d\u00e5rlige pr\u00e6station dikterer, hvordan vi omg\u00e5s hinanden. Sandhedsstandardteorien (TDT) siger, at folk typisk vil antage, at det, en person fort\u00e6ller dem, er sandt. De sociale omkostninger ved at kalde personen en l\u00f8gner er for stor en risiko med<\/p>","protected":false},"author":6,"featured_media":13339,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[103],"class_list":["post-13337","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-ai-debate"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI lie detector beats humans and could be socially disruptive | DailyAI<\/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:\/\/dailyai.com\/da\/2024\/07\/ai-lie-detector-beats-humans-and-could-be-socially-disruptive\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI lie detector beats humans and could be socially disruptive | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from the University of W\u00fcrzburg and the Max-Planck Institute for Human Development trained an AI model to detect lies and it could disrupt the way we engage with each other. Humans aren\u2019t great at telling whether a person is lying or telling the truth. Experiments show that our hit rate is around 50% at best and this poor performance dictates how we engage with each other. The truth-default theory (TDT) says that people will typically assume that what a person tells them is true. 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