{"id":1991,"date":"2023-06-22T21:18:13","date_gmt":"2023-06-22T21:18:13","guid":{"rendered":"https:\/\/dailyai.com\/?p=1991"},"modified":"2023-06-22T21:38:35","modified_gmt":"2023-06-22T21:38:35","slug":"stanford-researchers-test-ai-model-compliance-against-the-eu-ai-act","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/06\/stanford-researchers-test-ai-model-compliance-against-the-eu-ai-act\/","title":{"rendered":"Stanford-studie tester grunnleggende AI-modellers samsvar med EUs AI-lov"},"content":{"rendered":"

AI-forskere ved Stanford evaluerte 10 ledende AI-modellers samsvar med EUs foresl\u00e5tte AI-lov og fant store variasjoner, med generelt svake resultater over hele linjen.\u00a0<\/b><\/p>\n

Studien<\/a> Vi har kritisk unders\u00f8kt 10 AI-modeller opp mot 12 kriterier som er fastsatt i EUs lovforslag, og avdekket store variasjoner i etterlevelsen - ingen modeller oppfyller alle kriteriene.\u00a0<\/span><\/p>\n

Studien belyser avgrunnen mellom forventningene i AI-loven og det n\u00e5v\u00e6rende styringsarbeidet blant ledende AI-utviklere. <\/span><\/p>\n

For \u00e5 overholde loven m\u00e5 utbyggerne forbedre \u00e5penheten og risikoreduksjonsstrategiene sine betraktelig, noe som i noen tilfeller nesten helt mangler.\u00a0<\/span><\/p>\n

Hvordan studien ble gjennomf\u00f8rt<\/span><\/h2>\n

Forfatterne har gjennomf\u00f8rt en detaljert studie av AI-loven, og har trukket ut 22 krav. <\/span><\/p>\n

Av disse valgte de ut 12 som kunne evalueres p\u00e5 en meningsfull m\u00e5te ved hjelp av offentlig informasjon. Deretter laget de en fempunktsskala for hvert av disse 12 kravene.\u00a0<\/span><\/p>\n

Den \u00e5pne AI-modellen BLOOM, produsert av Hugging Face, kom ut som modellen med h\u00f8yest poengsum, med totalt 36 av 48 mulige poeng.\u00a0<\/span><\/p>\n

Google-st\u00f8ttede Anthropic og det tyske AI-selskapet Aleph Alpha gjorde det derimot betydelig d\u00e5rligere, med henholdsvis 7 og 5 poeng.\u00a0<\/span>ChatGPT havnet midt p\u00e5 treet med 25\/48.<\/span><\/p>\n

\"Grunnleggende
Stanfords poengsum for 10 grunnleggende modeller. Kreditt: Stanford<\/a>.<\/figcaption><\/figure>\n

De fire viktigste omr\u00e5dene for manglende samsvar er opphavsrettslig beskyttet data, energi, risikoreduksjon og evaluering\/testing.<\/span><\/p>\n

En av forfatterne, Kevin Klyman, forsker ved Stanfords Center for Research on Foundation Models, bemerket at de fleste utviklere ikke opplyser om sine risikoreduserende strategier, noe som kan v\u00e6re en \"dealbreaker\". Klyman sa: \"Tilbyderne oppgir ofte ikke hvor effektive deres risikoreduserende tiltak er, noe som betyr at vi ikke kan si noe om hvor risikable enkelte stiftelsesmodeller er.\"<\/span><\/p>\n

Dessuten er det stor variasjon i treningsdataene som brukes til \u00e5 trene modeller. EU vil kreve at AI-utviklere er mer \u00e5pne om datakildene sine, noe 4\/10 av utviklerne ikke gj\u00f8r. ChatGPT fikk bare 1 poeng p\u00e5 dette omr\u00e5det.\u00a0<\/span><\/p>\n

\u00c5pen kildekode versus propriet\u00e6re modeller<\/span><\/h2>\n

Rapporten avdekket ogs\u00e5 en klar todeling i samsvar avhengig av om en modell var \u00e5pen kildekode eller propriet\u00e6r.\u00a0<\/span><\/p>\n

Modeller med \u00e5pen kildekode oppn\u00e5dde gode resultater n\u00e5r det gjelder ressursopplysning og datakrav, men risikoen er i stor grad udokumentert.<\/span><\/p>\n

Propriet\u00e6re modeller er det motsatte - de er grundig testet og veldokumenterte med robuste strategier for risikoreduksjon, men de er ugjennomsiktige n\u00e5r det gjelder data og teknologirelaterte beregninger.\u00a0<\/span><\/p>\n

Eller for \u00e5 sette det p\u00e5 spissen: Open source-utviklere har ikke s\u00e5 mange konkurransehemmeligheter \u00e5 beskytte, men produktene deres er i seg selv mer risikofylte, ettersom de kan brukes og endres av nesten hvem som helst.\u00a0<\/span><\/p>\n

Private utviklere vil sannsynligvis holde deler av modellene sine under l\u00e5s og sl\u00e5, men de kan p\u00e5vise sikkerhet og risikoreduksjon. Selv Microsoft, OpenAIs hovedinvestor, er ikke helt forst\u00e5 hvordan OpenAIs modeller fungerer<\/a>.\u00a0<\/span><\/p>\n

Hva anbefaler studien?\u00a0<\/span><\/h2>\n

Forfatterne av studien erkjenner at gapet mellom EUs forventninger og virkeligheten er alarmerende, og kommer med flere anbefalinger til beslutningstakere og modellutviklere.<\/p>\n

Studien anbefaler EUs politiske beslutningstakere:<\/span><\/p>\n

    \n
  • Avgrense og spesifisere parametrene i EUs AI Act<\/b>: Forskerne hevder at AI-lovens tekniske spr\u00e5k og parametere er underspesifiserte.\u00a0<\/span><\/li>\n
  • Fremme \u00e5penhet og ansvarlighet<\/b>: Forskerne mener at de strengeste reglene b\u00f8r v\u00e6re ultra-m\u00e5lrettede mot de aller st\u00f8rste og mest dominerende utviklerne, noe som b\u00f8r f\u00f8re til en mer effektiv h\u00e5ndhevelse.\u00a0<\/span><\/li>\n
  • S\u00f8rg for tilstrekkelige ressurser til h\u00e5ndheving<\/b>: For at EUs AI Act skal kunne h\u00e5ndheves effektivt, b\u00f8r h\u00e5ndhevingsorganene f\u00e5 tilgang til tekniske ressurser og kompetanse.\u00a0<\/span><\/li>\n<\/ul>\n

    Studien anbefaler globale beslutningstakere:<\/span><\/p>\n

      \n
    • Prioriter \u00e5penhet<\/b>: Forskerne understreker at \u00e5penhet er avgj\u00f8rende og b\u00f8r v\u00e6re hovedfokuset i det politiske arbeidet. De hevder at erfaringene fra reguleringen av sosiale medier viser de skadelige konsekvensene av mangelfull \u00e5penhet, noe som ikke b\u00f8r gjentas i forbindelse med kunstig intelligens.\u00a0<\/span><\/li>\n
    • Avklare sp\u00f8rsm\u00e5l om opphavsrett<\/b>: Grensene for opphavsrett til AI-treningsdata og AI-resultater er omdiskutert. Med tanke p\u00e5 den lave overholdelsen som er observert n\u00e5r det gjelder \u00e5 offentliggj\u00f8re opphavsrettsbeskyttede treningsdata, argumenterer forskerne for at juridiske retningslinjer m\u00e5 spesifisere hvordan opphavsrett samhandler med treningsprosedyrer og resultatet av generative modeller. Dette inneb\u00e6rer blant annet \u00e5 definere vilk\u00e5rene for opphavsrett eller lisenser som m\u00e5 respekteres under oppl\u00e6ring, og \u00e5 fastsl\u00e5 hvordan maskingenerert innhold kan krenke opphavsretten.<\/span><\/li>\n<\/ul>\n

      Studien anbefaler utviklere av stiftelsesmodeller:<\/span><\/p>\n

        \n
      • Streber etter kontinuerlig forbedring<\/b>: Tilbyderne b\u00f8r hele tiden ha som m\u00e5l \u00e5 forbedre sin etterlevelse. St\u00f8rre leverand\u00f8rer, som OpenAI, b\u00f8r g\u00e5 foran med et godt eksempel og spre ressurser til nedstr\u00f8mskunder som f\u00e5r tilgang til modellene deres via API.\u00a0<\/span><\/li>\n
      • Talsmann for bransjestandarder<\/b>: Modellleverand\u00f8rer b\u00f8r bidra til \u00e5 etablere bransjestandarder, noe som kan f\u00f8re til et mer transparent og ansvarlig AI-\u00f8kosystem.<\/span><\/li>\n<\/ul>\n

        Selv om det er noen positive ting \u00e5 hente fra risiko- og overv\u00e5kingsstandardene som er etablert av ledende utviklere som OpenAI, er manglene p\u00e5 omr\u00e5der som opphavsrett langt fra ideelle.<\/p>\n

        N\u00e5r det gjelder \u00e5 anvende regulering p\u00e5 AI som en altomfattende kategori -<\/span> som kan vise seg \u00e5 bli sv\u00e6rt vanskelig - <\/span>ettersom kommersielle modeller og modeller med \u00e5pen kildekode er strukturelt forskjellige og vanskelige \u00e5 sl\u00e5 sammen.<\/p>","protected":false},"excerpt":{"rendered":"

        AI-forskere ved Stanford evaluerte 10 ledende AI-modellers samsvar med EUs foresl\u00e5tte AI-lov og fant store variasjoner, med generelt svake resultater over hele linjen. Studien unders\u00f8kte 10 AI-modeller kritisk opp mot 12 kriterier i EUs lovforslag, og avdekket store variasjoner i etterlevelse, og ingen av modellene oppfylte alle kravene. Studien belyser avgrunnen mellom forventningene i AI-loven og den n\u00e5v\u00e6rende styringsinnsatsen blant ledende AI-utviklere. For \u00e5 overholde loven m\u00e5 utviklerne bli langt bedre p\u00e5 \u00e5penhet og risikoreduserende strategier, noe som i noen tilfeller nesten helt mangler. Hvordan<\/p>","protected":false},"author":2,"featured_media":1993,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[129,145,120],"class_list":["post-1991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-policy","tag-ai-regulation","tag-ai-risk","tag-eu-ai-act"],"yoast_head":"\nStanford study tests foundational AI model compliance against the EU AI Act | DailyAI<\/title>\n<meta name=\"description\" content=\"AI researchers at Stanford evaluated the compliance of 10 leading AI models against the proposed EU AI Act and found wide variation.\" \/>\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\/2023\/06\/stanford-researchers-test-ai-model-compliance-against-the-eu-ai-act\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" 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