{"id":11908,"date":"2024-05-02T18:41:38","date_gmt":"2024-05-02T18:41:38","guid":{"rendered":"https:\/\/dailyai.com\/?p=11908"},"modified":"2024-05-04T17:24:35","modified_gmt":"2024-05-04T17:24:35","slug":"princeton-led-team-warns-that-machine-learning-brings-peril-to-science","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Princeton-ledet team sl\u00e5r alarm: AI udg\u00f8r en risiko for den videnskabelige integritet"},"content":{"rendered":"

AI forandrer den videnskabelige forskning, men uden ordentlig vejledning kan den g\u00f8re mere skade end gavn.<\/strong><\/p>\n

Det er den spidse konklusion af en <\/span>nyt papir<\/span><\/a> offentliggjort i Science Advances af et tv\u00e6rfagligt team p\u00e5 19 forskere under ledelse af datalogerne Arvind Narayanan og Sayash Kapoor fra Princeton University.\u00a0<\/span><\/p>\n

Teamet h\u00e6vder, at misbrug af maskinl\u00e6ring p\u00e5 tv\u00e6rs af videnskabelige discipliner giver n\u00e6ring til en reproducerbarhedskrise, der truer med at underminere selve grundlaget for videnskaben.<\/span><\/p>\n

\"N\u00e5r vi g\u00e5r fra traditionelle statistiske metoder til maskinl\u00e6ringsmetoder, er der langt flere m\u00e5der at skyde sig selv i foden p\u00e5\". <\/span>sagde Narayanan.<\/span><\/a>som leder Princetons Center for Information Technology Policy.\u00a0<\/span><\/p>\n

\"Hvis vi ikke griber ind for at forbedre vores videnskabelige standarder og rapporteringsstandarder, n\u00e5r det g\u00e6lder maskinl\u00e6ringsbaseret videnskab, risikerer vi, at ikke bare \u00e9n disciplin, men mange forskellige videnskabelige discipliner genopdager disse kriser den ene efter den anden.\"<\/span><\/p>\n

If\u00f8lge forfatterne er problemet, at maskinl\u00e6ring hurtigt er blevet taget i brug inden for n\u00e6sten alle videnskabelige omr\u00e5der, ofte uden klare standarder for at sikre resultaternes integritet og reproducerbarhed. <\/span><\/p>\n

De fremh\u00e6ver, at t<\/span>Der er allerede udgivet tusindvis af artikler, der bruger fejlbeh\u00e6ftede maskinl\u00e6ringsmetoder.<\/span><\/p>\n

Men det Princeton-ledede team siger, at der stadig er tid til at undg\u00e5 denne forest\u00e5ende krise. De har udarbejdet en simpel tjekliste over bedste praksis, som, hvis den bliver udbredt, kan sikre p\u00e5lideligheden af maskinl\u00e6ring inden for videnskaben.\u00a0<\/span><\/p>\n

Tjeklisten, der hedder REFORMS (Recommendations for Machine-learning-based Science), best\u00e5r af 32 sp\u00f8rgsm\u00e5l fordelt p\u00e5 otte n\u00f8gleomr\u00e5der:<\/span><\/p>\n

    \n
  1. M\u00e5l for unders\u00f8gelsen<\/b>: Angiv klart den videnskabelige p\u00e5stand, der frems\u00e6ttes, og hvordan maskinl\u00e6ring vil blive brugt til at underst\u00f8tte den. Begrund valget af maskinl\u00e6ring frem for traditionelle statistiske metoder.<\/span><\/li>\n
  2. Beregningsm\u00e6ssig reproducerbarhed<\/b>: Giv koden, data, specifikationer for computermilj\u00f8et, dokumentation og et reproduktionsscript, der er n\u00f8dvendigt for, at andre kan reproducere unders\u00f8gelsens resultater uafh\u00e6ngigt.<\/span><\/li>\n
  3. Datakvalitet<\/b>: Dokumenter datakilder, stikpr\u00f8veramme, resultatvariabler, stikpr\u00f8vest\u00f8rrelse og m\u00e6ngden af manglende data. Begrund, at datas\u00e6ttet er passende og repr\u00e6sentativt for det videnskabelige sp\u00f8rgsm\u00e5l.<\/span><\/li>\n
  4. Forbehandling af data<\/b>: Rapporter, hvordan data blev renset, omdannet og opdelt i tr\u00e6nings- og tests\u00e6t. Giv en begrundelse for eventuelle data, der blev udelukket.<\/span><\/li>\n
  5. Modellering<\/b>: Beskriv og begrund alle afpr\u00f8vede modeller, den metode, der blev brugt til at v\u00e6lge den\/de endelige model(ler), og hyperparameterindstillingsprocessen. Sammenlign performance med passende baselines.<\/span><\/li>\n
  6. L\u00e6kage af data<\/b>: Kontroll\u00e9r, at modelleringsprocessen ikke utilsigtet har brugt oplysninger fra testdataene, og at inputfunktioner ikke l\u00e6kker resultatet.<\/span><\/li>\n<\/ol>\n

    \"Det er et systematisk problem med systematiske l\u00f8sninger\", forklarer Kapoor. <\/span><\/p>\n

    Men omkostningerne ved at g\u00f8re det forkert<\/span> kan v\u00e6re enorme. Fejlbeh\u00e6ftet videnskab kan \u00f8del\u00e6gge lovende forskning, afskr\u00e6kke forskere og underminere offentlighedens tillid til videnskaben.\u00a0<\/span><\/p>\n

    Tidligere forskning, som f.eks. <\/span>Naturens store unders\u00f8gelse<\/span><\/a> af akademikere om generativ AI i videnskaben, viste, at AI's dybere og progressive integration i videnskabelige arbejdsgange er uundg\u00e5elig. <\/span><\/p>\n

    Deltagerne fremh\u00e6vede masser af fordele - 66% bem\u00e6rkede, at AI muligg\u00f8r hurtigere databehandling, 58% mente, at det forbedrer beregningerne, og 55% sagde, at det sparer tid og penge.<\/span>\u00a0<\/span><\/p>\n

    Men 53% mente, at resultaterne ikke kunne reproduceres, 58% var bekymrede for bias, og 55% mente, at AI kunne muligg\u00f8re svigagtig forskning.\u00a0<\/span><\/p>\n

    Vi s\u00e5 beviser p\u00e5 dette, da forskere <\/span>udgav en artikel<\/span><\/a> med nonsens AI-genererede diagrammer i tidsskriftet Frontiers - en rotte med gigantiske testikler, intet mindre. Komisk, men det viste, hvordan peer review m\u00e5ske ikke engang fanger \u00e5benlyse anvendelser af AI.\u00a0<\/span><\/p>\n

    I sidste ende er AI som ethvert v\u00e6rkt\u00f8j kun s\u00e5 sikkert og effektivt som mennesket bag det. Uforsigtig brug, selv om det er utilsigtet, kan f\u00f8re videnskaben p\u00e5 afveje.<\/span><\/p>\n

    De nye retningslinjer har til form\u00e5l at holde \"\u00e6rlige mennesker \u00e6rlige\", som Narayanan udtrykte det. <\/span><\/p>\n

    Udbredt anvendelse blandt forskere, bed\u00f8mmere og tidsskrifter kan s\u00e6tte en ny standard for videnskabelig integritet i AI's tidsalder.<\/span><\/p>\n

    Men det bliver en udfordring at skabe konsensus, is\u00e6r fordi reproducerbarhedskrisen allerede flyver under radaren.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Kunstig intelligens forandrer den videnskabelige forskning, men uden ordentlig vejledning kan den g\u00f8re mere skade end gavn. Det er den skarpe konklusion i en ny artikel, der er offentliggjort i Science Advances af et tv\u00e6rfagligt team p\u00e5 19 forskere under ledelse af datalogerne Arvind Narayanan og Sayash Kapoor fra Princeton University. Holdet h\u00e6vder, at misbrug af maskinl\u00e6ring p\u00e5 tv\u00e6rs af videnskabelige discipliner giver n\u00e6ring til en reproducerbarhedskrise, der truer med at underminere selve grundlaget for videnskaben. \"N\u00e5r vi g\u00e5r fra traditionelle statistiske metoder til maskinl\u00e6ringsmetoder, er der langt flere m\u00e5der at skyde sig selv i foden p\u00e5,\" siger Narayanan, som<\/p>","protected":false},"author":2,"featured_media":11910,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105,135],"class_list":["post-11908","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning","tag-science"],"yoast_head":"\nPrinceton-led team sounds the alarm: AI poses risks to scientific integrity | 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\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Princeton-led team sounds the alarm: AI poses risks to scientific integrity | DailyAI\" \/>\n<meta property=\"og:description\" content=\"AI is transforming scientific research, but without proper guidance, it may do more harm than good. That’s the pointed conclusion of a new paper published in Science Advances by an interdisciplinary team of 19 researchers led by Princeton University computer scientists Arvind Narayanan and Sayash Kapoor.\u00a0 The team argues that the misuse of machine learning across scientific disciplines is fueling a reproducibility crisis that threatens to undermine the very foundations of science. “When we graduate from traditional statistical methods to machine learning methods, there are a vastly greater number of ways to shoot oneself in the foot,” said Narayanan, who\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-02T18:41:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-04T17:24:35+00:00\" \/>\n<meta property=\"og:image\" 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