{"id":6804,"date":"2023-10-26T19:21:21","date_gmt":"2023-10-26T19:21:21","guid":{"rendered":"https:\/\/dailyai.com\/?p=6804"},"modified":"2023-10-26T21:16:11","modified_gmt":"2023-10-26T21:16:11","slug":"new-research-into-datasets-reveals-systemic-ethical-and-legal-issues","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/10\/new-research-into-datasets-reveals-systemic-ethical-and-legal-issues\/","title":{"rendered":"Ny forskning p\u00e5 datasett avdekker systemiske etiske og juridiske problemer"},"content":{"rendered":"

AI dreier seg om data, men hvor kommer de fra? Er datasettene lovlige og etiske? Hvordan kan utviklere avgj\u00f8re det med sikkerhet?\u00a0<\/b><\/p>\n

Oppl\u00e6ring av maskinl\u00e6ringsmodeller, for eksempel store spr\u00e5kmodeller (LLM), krever store mengder tekstdata.\u00a0<\/span><\/p>\n

Det finnes stabler av datasett tilgjengelig p\u00e5 plattformer som Kaggle, GitHub og Hugging Face, men de befinner seg i en juridisk og etisk gr\u00e5sone, f\u00f8rst og fremst p\u00e5 grunn av lisensiering og sp\u00f8rsm\u00e5l om rettferdig bruk.\u00a0<\/span><\/p>\n

Den Data Provenance Initiative<\/a>et samarbeid mellom AI-forskere og jurister, gjennomgikk tusenvis av datasett for \u00e5 kaste lys over deres sanne opprinnelse. <\/span><\/p>\n

Det <\/span>fokuserte p\u00e5 over 1800 datasett som var tilgjengelige p\u00e5 plattformer som Hugging Face, GitHub og Papers With Code. <\/span>Datasettene er hovedsakelig utviklet for \u00e5 finjustere modeller med \u00e5pen kildekode, som Llama-2.\u00a0<\/span><\/p>\n

Studien avsl\u00f8rte at omtrent 70% av disse datasettene enten manglet tydelig lisensinformasjon eller var merket med altfor permissive lisenser.\u00a0<\/span><\/p>\n

Med en skrikende mangel p\u00e5 klarhet rundt opphavsrett og begrensninger for kommersiell bruk risikerer AI-utviklere \u00e5 bryte loven eller opphavsretten ved et uhell.<\/span><\/p>\n

Shayne Longpre, en doktorgradskandidat ved MIT Media Lab som ledet revisjonen, understreket at problemet ikke skyldes hostingplattformene, men snarere er et systemisk problem i maskinl\u00e6ringssamfunnet.<\/span><\/p>\n

2023 har v\u00e6rt vitne til en flodb\u00f8lge av s\u00f8ksm\u00e5l<\/a> rettet mot store AI-utviklere som Meta, Anthropic og OpenAI, som er under sterkt press for \u00e5 innf\u00f8re en mer \u00e5pen datainnsamlingspraksis. Reguleringer, som f.eks. EUs lov om kunstig intelligens<\/a>er satt til \u00e5 h\u00e5ndheve nettopp dette.\u00a0<\/span><\/p>\n

Data Provenance Initiative gj\u00f8r det mulig for maskinl\u00e6ringsutviklere \u00e5 <\/span>Utforsk de reviderte datasettene her<\/span><\/a>. Initiativet analyserer ogs\u00e5 m\u00f8nstre i datasettene, og belyser deres geografiske og institusjonelle opprinnelse.\u00a0<\/span><\/p>\n

De fleste datasettene er konstruert i det engelskspr\u00e5klige globale nord, noe som fremhever sosiokulturelle ubalanser.\u00a0<\/span><\/p>\n

\"Dataproveniens
Data Provenance Initiative fant at datasettene hovedsakelig representerer engelskspr\u00e5klige land og det globale nord. Kilde: Data Provenance.org<\/a>.<\/figcaption><\/figure>\n

Mer om studien<\/span><\/h2>\n

Denne storstilte analysen av datasett avdekket systematiske problemer med hvordan data samles inn og distribueres. Initiativet har ogs\u00e5 utarbeidet en artikkel for \u00e5 forklare funnene, <\/span>publisert her<\/span><\/a>.<\/span><\/p>\n

Her finner du mer informasjon om studiens metoder og funn:<\/span><\/p>\n

    \n
  1. Analyserer datasett for opprinnelse og merking<\/b>: I denne studien ble over 1800 finjusteringsdatasett systematisk gjennomg\u00e5tt for \u00e5 granske dataenes opprinnelse, lisensiering og dokumentasjon.\u00a0<\/span><\/li>\n
  2. Bevis p\u00e5 feilmerking<\/b>: Funnene viste at det er store forskjeller mellom datatypene som er tilgjengelige under ulike lisenser, og hvilke konsekvenser dette har for juridiske tolkninger av opphavsrett og rettferdig bruk. Det ble avdekket en h\u00f8y grad av feilkategorisering av lisenser, med over 72% av datasettene som ikke spesifiserte en lisens, og en feilprosent p\u00e5 50% i de som gjorde det.<\/span><\/li>\n
  3. Up\u00e5litelig dataproveniens<\/b>: Forskningen retter oppmerksomheten mot problemet med up\u00e5litelig dataproveniens, og understreker behovet for standarder for \u00e5 spore datalinje, sikre riktig attribusjon og oppmuntre til ansvarlig bruk av data.\u00a0<\/span><\/li>\n
  4. Geografisk utbredelse: <\/b>Studien belyser en alvorlig mangel p\u00e5 representasjon og attribusjon for datasett som stammer fra det globale s\u00f8r. De fleste datasettene er engelskspr\u00e5klige og kulturelt knyttet til Europa, Nord-Amerika og det engelskspr\u00e5klige Oseania.\u00a0<\/span><\/li>\n<\/ol>\n

    Denne studien belyser systemiske og strukturelle problemer i hvordan data skapes, distribueres og brukes. Data er en kritisk ressurs for AI, og i likhet med naturressurser er de begrensede.\u00a0<\/span><\/p>\n

    Det er bekymring for at AI-teknologien etter hvert vil vokse seg st\u00f8rre enn dagens datasett, og kanskje til og med <\/span>begynner \u00e5 forbruke sin egen produksjon<\/span><\/a>Det betyr at AI-modeller vil l\u00e6re av AI-generert tekst.\u00a0<\/span><\/p>\n

    Dette kan svekke kvaliteten p\u00e5 modellene, noe som betyr at etiske og lovlige data av h\u00f8y kvalitet kan bli sv\u00e6rt verdifulle. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    AI dreier seg om data, men hvor kommer de fra? Er datasettene lovlige og etiske? Hvordan kan utviklere avgj\u00f8re det med sikkerhet? Oppl\u00e6ring av maskinl\u00e6ringsmodeller, for eksempel store spr\u00e5kmodeller (LLM), krever store mengder tekstdata. Det finnes stabler av datasett tilgjengelig p\u00e5 plattformer som Kaggle, GitHub og Hugging Face, men de befinner seg i en juridisk og etisk gr\u00e5sone, f\u00f8rst og fremst p\u00e5 grunn av lisensiering og sp\u00f8rsm\u00e5l om rettferdig bruk. Data Provenance Initiative, et samarbeid mellom AI-forskere og jurister, har gjennomg\u00e5tt tusenvis av datasett for \u00e5 kaste lys over deres sanne opprinnelse. De fokuserte p\u00e5 over 1 800<\/p>","protected":false},"author":2,"featured_media":6806,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[454,453,105],"class_list":["post-6804","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-data","tag-datasets","tag-machine-learning"],"yoast_head":"\nNew research into datasets reveals systemic ethical and legal issues | DailyAI<\/title>\n<meta name=\"description\" content=\"AI revolves around data, but where does it come from? Is it legal to use? 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