{"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\/nb\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Princeton-ledet team sl\u00e5r alarm: AI utgj\u00f8r en risiko for vitenskapelig integritet"},"content":{"rendered":"

Kunstig intelligens er i ferd med \u00e5 forandre vitenskapelig forskning, men uten riktig veiledning kan den gj\u00f8re mer skade enn nytte.<\/strong><\/p>\n

Det er den spissformulerte konklusjonen i en <\/span>nytt papir<\/span><\/a> publisert i Science Advances av et tverrfaglig team p\u00e5 19 forskere ledet av dataforskerne Arvind Narayanan og Sayash Kapoor ved Princeton University.\u00a0<\/span><\/p>\n

Teamet hevder at misbruk av maskinl\u00e6ring p\u00e5 tvers av vitenskapelige disipliner gir n\u00e6ring til en reproduserbarhetskrise som truer med \u00e5 undergrave selve grunnlaget for vitenskapen.<\/span><\/p>\n

\"N\u00e5r vi g\u00e5r fra tradisjonelle statistiske metoder til maskinl\u00e6ringsmetoder, er det langt flere m\u00e5ter \u00e5 skyte seg selv i foten p\u00e5\", sier han. <\/span>sa Narayanan<\/span><\/a>, som leder Princetons senter for informasjonsteknologi.\u00a0<\/span><\/p>\n

\"Hvis vi ikke griper inn for \u00e5 forbedre de vitenskapelige standardene og rapporteringsstandardene v\u00e5re n\u00e5r det gjelder maskinl\u00e6ringsbasert forskning, risikerer vi at ikke bare \u00e9n disiplin, men mange ulike vitenskapelige disipliner gjenoppdager disse krisene, den ene etter den andre.\"<\/span><\/p>\n

If\u00f8lge forfatterne er problemet at maskinl\u00e6ring raskt har blitt tatt i bruk p\u00e5 nesten alle vitenskapelige felt, ofte uten klare standarder for \u00e5 sikre integriteten og reproduserbarheten av resultatene. <\/span><\/p>\n

De fremhever at t<\/span>Det er allerede publisert tusenvis av artikler som bruker feilaktige maskinl\u00e6ringsmetoder.<\/span><\/p>\n

Men teamet fra Princeton sier at det fortsatt er tid til \u00e5 unng\u00e5 denne forest\u00e5ende krisen. De har lagt frem en enkel sjekkliste over beste praksis som, hvis den blir tatt i bruk, kan sikre p\u00e5liteligheten til maskinl\u00e6ring i vitenskapen.\u00a0<\/span><\/p>\n

Sjekklisten, kalt REFORMS (Recommendations for Machine-learning-based Science), best\u00e5r av 32 sp\u00f8rsm\u00e5l fordelt p\u00e5 \u00e5tte n\u00f8kkelomr\u00e5der:<\/span><\/p>\n

    \n
  1. M\u00e5l for studien<\/b>: Gj\u00f8r klart rede for den vitenskapelige p\u00e5standen som fremsettes, og hvordan maskinl\u00e6ring vil bli brukt til \u00e5 underbygge den. Begrunn valget av maskinl\u00e6ring fremfor tradisjonelle statistiske metoder.<\/span><\/li>\n
  2. Reproduserbarhet i beregningene<\/b>: Lever koden, dataene, spesifikasjonene for databehandlingsmilj\u00f8et, dokumentasjonen og et reproduksjonsskript som er n\u00f8dvendig for at andre skal kunne reprodusere studiens resultater p\u00e5 egen h\u00e5nd.<\/span><\/li>\n
  3. Datakvalitet<\/b>: Dokumenter datakildene, utvalgsrammen, utfallsvariablene, utvalgsst\u00f8rrelsen og mengden manglende data. Begrunn at datasettet er hensiktsmessig og representativt for den vitenskapelige problemstillingen.<\/span><\/li>\n
  4. Forbehandling av data<\/b>: Rapporter hvordan data ble renset, transformert og delt inn i trenings- og testsett. Gi en begrunnelse for eventuelle data som ble ekskludert.<\/span><\/li>\n
  5. Modellering<\/b>: Beskriv og begrunn alle modeller som er pr\u00f8vd, metoden som ble brukt for \u00e5 velge de(n) endelige modellen(e), og hyperparameterinnstillingsprosessen. Sammenlign ytelsen mot passende referanseverdier.<\/span><\/li>\n
  6. Datalekkasje<\/b>: Kontroller at modelleringsprosessen ikke utilsiktet har brukt informasjon fra testdataene, og at inndatafunksjonene ikke lekker utfallet.<\/span><\/li>\n<\/ol>\n

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

    Men kostnadene ved \u00e5 gj\u00f8re feil<\/span> kan v\u00e6re enorme. Feilaktig forskning kan f\u00f8re til at lovende forskning g\u00e5r i vasken, at forskere mister motet, og at allmennhetens tillit til vitenskapen svekkes.\u00a0<\/span><\/p>\n

    Tidligere forskning, som for eksempel <\/span>Naturens storstilte unders\u00f8kelse<\/span><\/a> av akademikere om generativ AI i vitenskapen, indikerte at en dypere og mer progressiv integrering av AI i vitenskapelige arbeidsflyter er uunng\u00e5elig. <\/span><\/p>\n

    Deltakerne fremhevet en rekke fordeler - 66% p\u00e5pekte at AI muliggj\u00f8r raskere databehandling, 58% mente at det forbedrer beregningene, og 55% sa at det sparer tid og penger.<\/span>\u00a0<\/span><\/p>\n

    53% mente imidlertid at resultatene kunne v\u00e6re umulige \u00e5 gjenskape, 58% var bekymret for skjevheter, og 55% mente at kunstig intelligens kunne muliggj\u00f8re uredelig forskning.\u00a0<\/span><\/p>\n

    Vi observerte bevis p\u00e5 dette da forskere <\/span>publiserte en artikkel<\/span><\/a> med t\u00e5pelige AI-genererte diagrammer i tidsskriftet Frontiers - en rotte med gigantiske testikler, intet mindre. Komisk, men det viste hvordan fagfellevurdering kanskje ikke engang fanger opp \u00e5penbar bruk av kunstig intelligens.\u00a0<\/span><\/p>\n

    Som alle andre verkt\u00f8y er kunstig intelligens til syvende og sist bare s\u00e5 trygt og effektivt som mennesket bak det. Uforsiktig bruk, selv om det ikke er tilsiktet, kan f\u00f8re vitenskapen p\u00e5 villspor.<\/span><\/p>\n

    De nye retningslinjene tar sikte p\u00e5 \u00e5 holde \"\u00e6rlige mennesker \u00e6rlige\", som Narayanan uttrykte det. <\/span><\/p>\n

    Hvis forskere, fagfeller og tidsskrifter tar det i bruk i stor skala, kan det sette en ny standard for vitenskapelig integritet i AI-tidsalderen.<\/span><\/p>\n

    Det vil imidlertid v\u00e6re utfordrende \u00e5 skape konsensus, s\u00e6rlig siden reproduserbarhetskrisen allerede flyr under radaren.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Kunstig intelligens er i ferd med \u00e5 forandre vitenskapelig forskning, men uten riktig veiledning kan den gj\u00f8re mer skade enn nytte. Det er den spissformulerte konklusjonen i en ny artikkel publisert i Science Advances av et tverrfaglig team p\u00e5 19 forskere ledet av informatikerne Arvind Narayanan og Sayash Kapoor ved Princeton University. Teamet hevder at misbruk av maskinl\u00e6ring p\u00e5 tvers av vitenskapelige disipliner gir n\u00e6ring til en reproduserbarhetskrise som truer med \u00e5 undergrave selve grunnlaget for vitenskapen. \"N\u00e5r vi g\u00e5r fra tradisjonelle statistiske metoder til maskinl\u00e6ringsmetoder, er det langt flere m\u00e5ter \u00e5 skyte seg selv i foten p\u00e5\", sier 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\/nb\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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\/nb\/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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