{"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\/fr\/2023\/10\/new-research-into-datasets-reveals-systemic-ethical-and-legal-issues\/","title":{"rendered":"De nouvelles recherches sur les ensembles de donn\u00e9es r\u00e9v\u00e8lent des probl\u00e8mes \u00e9thiques et juridiques syst\u00e9miques"},"content":{"rendered":"

L'IA s'articule autour des donn\u00e9es, mais d'o\u00f9 viennent-elles ? Les ensembles de donn\u00e9es sont-ils l\u00e9gaux et \u00e9thiques ? Comment les d\u00e9veloppeurs peuvent-ils s'en assurer ?\u00a0<\/b><\/p>\n

La formation de mod\u00e8les d'apprentissage automatique tels que les grands mod\u00e8les de langage (LLM) n\u00e9cessite de grands volumes de donn\u00e9es textuelles.\u00a0<\/span><\/p>\n

Des piles de donn\u00e9es sont disponibles sur des plateformes telles que Kaggle, GitHub et Hugging Face, mais elles existent dans une zone d'ombre juridique et \u00e9thique, principalement en raison des questions de licence et d'utilisation \u00e9quitable.\u00a0<\/span><\/p>\n

Les Initiative sur la provenance des donn\u00e9es<\/a>un effort de collaboration entre des chercheurs en IA et des professionnels du droit, a examin\u00e9 des milliers d'ensembles de donn\u00e9es afin de faire la lumi\u00e8re sur leurs v\u00e9ritables origines. <\/span><\/p>\n

Il <\/span>s'est concentr\u00e9e sur plus de 1 800 ensembles de donn\u00e9es disponibles sur des plateformes telles que Hugging Face, GitHub et Papers With Code. <\/span>Les ensembles de donn\u00e9es sont principalement con\u00e7us pour affiner les mod\u00e8les \u00e0 source ouverte tels que Llama-2.\u00a0<\/span><\/p>\n

L'\u00e9tude a r\u00e9v\u00e9l\u00e9 qu'environ 70% de ces ensembles de donn\u00e9es ne contenaient pas d'informations claires sur les licences ou \u00e9taient marqu\u00e9es par des licences trop permissives.\u00a0<\/span><\/p>\n

En raison d'un manque flagrant de clart\u00e9 concernant les restrictions en mati\u00e8re de droits d'auteur et d'utilisation commerciale, les d\u00e9veloppeurs d'IA risquent d'enfreindre accidentellement la loi ou de violer les droits d'auteur.<\/span><\/p>\n

Shayne Longpre, doctorant au MIT Media Lab qui a dirig\u00e9 l'audit, a soulign\u00e9 que le probl\u00e8me n'est pas imputable aux plateformes d'h\u00e9bergement, mais qu'il s'agit plut\u00f4t d'un probl\u00e8me syst\u00e9mique au sein de la communaut\u00e9 de l'apprentissage automatique.<\/span><\/p>\n

L'ann\u00e9e 2023 a \u00e9t\u00e9 marqu\u00e9e par une d\u00e9luge de poursuites judiciaires<\/a> ciblant les principaux d\u00e9veloppeurs d'IA tels que Meta, Anthropic et OpenAI, qui sont soumis \u00e0 une pression extr\u00eame pour adopter des pratiques plus transparentes en mati\u00e8re de collecte de donn\u00e9es. Les r\u00e9glementations, telles que la Loi sur l'IA de l'UE<\/a>sont pr\u00eats \u00e0 mettre en \u0153uvre pr\u00e9cis\u00e9ment cela.\u00a0<\/span><\/p>\n

L'initiative sur la provenance des donn\u00e9es permet aux d\u00e9veloppeurs d'apprentissage automatique de <\/span>explorer les ensembles de donn\u00e9es audit\u00e9es ici<\/span><\/a>. L'initiative analyse \u00e9galement les tendances au sein des ensembles de donn\u00e9es, en mettant en lumi\u00e8re leurs origines g\u00e9ographiques et institutionnelles.\u00a0<\/span><\/p>\n

La plupart des ensembles de donn\u00e9es sont construits dans le Nord anglophone, ce qui met en \u00e9vidence les d\u00e9s\u00e9quilibres socioculturels.\u00a0<\/span><\/p>\n

\"Provenance
L'initiative sur la provenance des donn\u00e9es a r\u00e9v\u00e9l\u00e9 que les ensembles de donn\u00e9es repr\u00e9sentent principalement les pays anglophones et le Nord global. Source : Donn\u00e9es Provenance.org<\/a>.<\/figcaption><\/figure>\n

En savoir plus sur l'\u00e9tude<\/span><\/h2>\n

Cette analyse \u00e0 grande \u00e9chelle des ensembles de donn\u00e9es a mis en \u00e9vidence des probl\u00e8mes syst\u00e9matiques li\u00e9s \u00e0 la mani\u00e8re dont les donn\u00e9es sont collect\u00e9es et distribu\u00e9es. L'initiative a \u00e9galement produit un document expliquant ses conclusions, <\/span>publi\u00e9 ici<\/span><\/a>.<\/span><\/p>\n

Voici plus d'informations sur les m\u00e9thodes et les r\u00e9sultats de l'\u00e9tude :<\/span><\/p>\n

    \n
  1. Analyse des ensembles de donn\u00e9es pour d\u00e9terminer l'origine et l'\u00e9tiquetage<\/b>: Cette \u00e9tude a syst\u00e9matiquement v\u00e9rifi\u00e9 plus de 1800 ensembles de donn\u00e9es de r\u00e9glage fin afin d'examiner minutieusement la provenance des donn\u00e9es, les licences et la documentation.\u00a0<\/span><\/li>\n
  2. Preuves d'erreur d'\u00e9tiquetage<\/b>: Les r\u00e9sultats ont mis en \u00e9vidence l'\u00e9cart entre les types de donn\u00e9es disponibles sous diff\u00e9rentes licences et les implications pour les interpr\u00e9tations juridiques du droit d'auteur et de l'utilisation \u00e9quitable. L'\u00e9tude a mis en \u00e9vidence un taux \u00e9lev\u00e9 de cat\u00e9gorisation erron\u00e9e des licences, avec plus de 72% d'ensembles de donn\u00e9es ne sp\u00e9cifiant pas de licence et un taux d'erreur de 50% dans ceux qui en sp\u00e9cifient une.<\/span><\/li>\n
  3. Provenance des donn\u00e9es non fiable<\/b>: La recherche attire l'attention sur la question du manque de fiabilit\u00e9 de la provenance des donn\u00e9es, en soulignant la n\u00e9cessit\u00e9 d'\u00e9tablir des normes pour retracer l'origine des donn\u00e9es, garantir une attribution correcte et encourager une utilisation responsable des donn\u00e9es.\u00a0<\/span><\/li>\n
  4. R\u00e9partition g\u00e9ographique : <\/b>L'\u00e9tude met en \u00e9vidence un grave manque de repr\u00e9sentation et d'attribution pour les ensembles de donn\u00e9es provenant du Sud global. La plupart des ensembles de donn\u00e9es tournent autour de la langue anglaise et sont culturellement li\u00e9s \u00e0 l'Europe, \u00e0 l'Am\u00e9rique du Nord et \u00e0 l'Oc\u00e9anie anglophone.\u00a0<\/span><\/li>\n<\/ol>\n

    Cette \u00e9tude met en \u00e9vidence des probl\u00e8mes syst\u00e9miques et structurels dans la mani\u00e8re dont les donn\u00e9es sont cr\u00e9\u00e9es, distribu\u00e9es et utilis\u00e9es. Les donn\u00e9es sont une ressource essentielle pour l'IA et, \u00e0 l'instar des ressources naturelles, elles sont limit\u00e9es.\u00a0<\/span><\/p>\n

    On craint que la technologie de l'IA ne finisse par d\u00e9passer les ensembles de donn\u00e9es actuels, voire qu'elle ne devienne une menace pour la sant\u00e9 publique. <\/span>commence \u00e0 consommer sa propre production<\/span><\/a>Cela signifie que les mod\u00e8les d'IA apprendront \u00e0 partir de textes g\u00e9n\u00e9r\u00e9s par l'IA.\u00a0<\/span><\/p>\n

    Cela pourrait \u00e9roder la qualit\u00e9 des mod\u00e8les, ce qui signifie que des donn\u00e9es de haute qualit\u00e9, \u00e9thiques et l\u00e9gales pourraient devenir tr\u00e8s pr\u00e9cieuses. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    L'IA s'articule autour des donn\u00e9es, mais d'o\u00f9 viennent-elles ? Les ensembles de donn\u00e9es sont-ils l\u00e9gaux et \u00e9thiques ? Comment les d\u00e9veloppeurs peuvent-ils s'en assurer ? L'apprentissage de mod\u00e8les d'apprentissage automatique tels que les grands mod\u00e8les de langage (LLM) n\u00e9cessite de grands volumes de donn\u00e9es textuelles. Des piles de donn\u00e9es sont disponibles sur des plateformes telles que Kaggle, GitHub et Hugging Face, mais elles existent dans une zone grise juridique et \u00e9thique, principalement en raison des questions de licence et d'utilisation \u00e9quitable. La Data Provenance Initiative, un effort de collaboration entre des chercheurs en IA et des professionnels du droit, a examin\u00e9 des milliers d'ensembles de donn\u00e9es pour faire la lumi\u00e8re sur leurs v\u00e9ritables origines. Elle s'est concentr\u00e9e sur plus de 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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