{"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\/fr\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Une \u00e9quipe dirig\u00e9e par Princeton tire la sonnette d'alarme : L'IA pr\u00e9sente des risques pour l'int\u00e9grit\u00e9 scientifique"},"content":{"rendered":"

L'IA transforme la recherche scientifique, mais si elle n'est pas correctement encadr\u00e9e, elle risque de faire plus de mal que de bien.<\/strong><\/p>\n

C'est la conclusion d'une \u00e9tude de l <\/span>nouveau document<\/span><\/a> publi\u00e9e dans Science Advances par une \u00e9quipe interdisciplinaire de 19 chercheurs dirig\u00e9e par les informaticiens Arvind Narayanan et Sayash Kapoor de l'universit\u00e9 de Princeton.\u00a0<\/span><\/p>\n

L'\u00e9quipe affirme que l'utilisation abusive de l'apprentissage automatique dans les disciplines scientifiques alimente une crise de la reproductibilit\u00e9 qui menace de saper les fondements m\u00eames de la science.<\/span><\/p>\n

\"Lorsque nous passons des m\u00e9thodes statistiques traditionnelles aux m\u00e9thodes d'apprentissage automatique, les possibilit\u00e9s de se tirer une balle dans le pied sont beaucoup plus nombreuses\". <\/span>a d\u00e9clar\u00e9 Narayanan<\/span><\/a>qui dirige le centre de Princeton pour la politique en mati\u00e8re de technologies de l'information.\u00a0<\/span><\/p>\n

\"Si nous n'intervenons pas pour am\u00e9liorer nos normes scientifiques et nos normes de rapport lorsqu'il s'agit de science bas\u00e9e sur l'apprentissage automatique, nous risquons de voir non pas une seule discipline, mais de nombreuses disciplines scientifiques diff\u00e9rentes red\u00e9couvrir ces crises l'une apr\u00e8s l'autre.\"<\/span><\/p>\n

Selon les auteurs, le probl\u00e8me est que l'apprentissage automatique a \u00e9t\u00e9 rapidement adopt\u00e9 par presque tous les domaines scientifiques, souvent sans normes claires pour garantir l'int\u00e9grit\u00e9 et la reproductibilit\u00e9 des r\u00e9sultats. <\/span><\/p>\n

Ils soulignent que les t<\/span>Des milliers d'articles utilisant des m\u00e9thodes d'apprentissage automatique erron\u00e9es ont d\u00e9j\u00e0 \u00e9t\u00e9 publi\u00e9s.<\/span><\/p>\n

Mais l'\u00e9quipe de Princeton affirme qu'il est encore temps d'\u00e9viter cette crise imminente. Elle a propos\u00e9 une simple liste de bonnes pratiques qui, si elles sont largement adopt\u00e9es, pourraient pr\u00e9server la fiabilit\u00e9 de l'apprentissage automatique dans le domaine scientifique.\u00a0<\/span><\/p>\n

La liste de contr\u00f4le, appel\u00e9e REFORMS (Recommendations for Machine-learning-based Science), comprend 32 questions portant sur huit domaines cl\u00e9s :<\/span><\/p>\n

    \n
  1. Objectifs de l'\u00e9tude<\/b>: \u00c9noncer clairement l'affirmation scientifique et la mani\u00e8re dont l'apprentissage automatique sera utilis\u00e9 pour l'\u00e9tayer. Justifier le choix de l'apprentissage automatique par rapport aux m\u00e9thodes statistiques traditionnelles.<\/span><\/li>\n
  2. Reproductibilit\u00e9 informatique<\/b>: Fournir le code, les donn\u00e9es, les sp\u00e9cifications de l'environnement informatique, la documentation et le script de reproduction n\u00e9cessaires pour que d'autres personnes puissent reproduire les r\u00e9sultats de l'\u00e9tude de mani\u00e8re ind\u00e9pendante.<\/span><\/li>\n
  3. Qualit\u00e9 des donn\u00e9es<\/b>: Documenter les sources de donn\u00e9es, le cadre d'\u00e9chantillonnage, les variables de r\u00e9sultats, la taille de l'\u00e9chantillon et la quantit\u00e9 de donn\u00e9es manquantes. Justifier que l'ensemble de donn\u00e9es est appropri\u00e9 et repr\u00e9sentatif de la question scientifique.<\/span><\/li>\n
  4. Pr\u00e9traitement des donn\u00e9es<\/b>: Indiquez comment les donn\u00e9es ont \u00e9t\u00e9 nettoy\u00e9es, transform\u00e9es et divis\u00e9es en ensembles de formation et de test. Justifiez les donn\u00e9es qui ont \u00e9t\u00e9 exclues.<\/span><\/li>\n
  5. Mod\u00e9lisation<\/b>: D\u00e9crire et justifier tous les mod\u00e8les essay\u00e9s, la m\u00e9thode utilis\u00e9e pour s\u00e9lectionner le(s) mod\u00e8le(s) final(aux) et le processus de r\u00e9glage des hyperparam\u00e8tres. Comparer les performances avec des lignes de base appropri\u00e9es.<\/span><\/li>\n
  6. Fuite de donn\u00e9es<\/b>: V\u00e9rifier que le processus de mod\u00e9lisation n'a pas utilis\u00e9 par inadvertance des informations provenant des donn\u00e9es de test et que les caract\u00e9ristiques d'entr\u00e9e ne donnent pas lieu \u00e0 des fuites sur le r\u00e9sultat.<\/span><\/li>\n<\/ol>\n

    \"Il s'agit d'un probl\u00e8me syst\u00e9matique avec des solutions syst\u00e9matiques\", explique M. Kapoor. <\/span><\/p>\n

    Cependant, les co\u00fbts d'une erreur<\/span> pourrait \u00eatre immense. Une science erron\u00e9e pourrait faire \u00e9chouer des recherches prometteuses, d\u00e9courager les chercheurs et \u00e9roder la confiance du public dans la science.\u00a0<\/span><\/p>\n

    Des recherches ant\u00e9rieures, telles que <\/span>L'enqu\u00eate \u00e0 grande \u00e9chelle de la nature<\/span><\/a> d'universitaires sur l'IA g\u00e9n\u00e9rative en science, a indiqu\u00e9 que l'int\u00e9gration plus profonde et progressive de l'IA dans les flux de travail scientifiques est in\u00e9vitable. <\/span><\/p>\n

    Les participants ont soulign\u00e9 de nombreux avantages - 66% ont not\u00e9 que l'IA permet un traitement plus rapide des donn\u00e9es, 58% pensent qu'elle am\u00e9liore les calculs, et 55% disent qu'elle permet d'\u00e9conomiser du temps et de l'argent.<\/span>\u00a0<\/span><\/p>\n

    Cependant, 53% estiment que les r\u00e9sultats pourraient ne pas \u00eatre reproductibles, 58% s'inqui\u00e8tent de la partialit\u00e9 et 55% pensent que l'IA pourrait permettre des recherches frauduleuses.\u00a0<\/span><\/p>\n

    Nous en avons eu la preuve lorsque les chercheurs <\/span>a publi\u00e9 un article<\/span><\/a> avec des diagrammes absurdes g\u00e9n\u00e9r\u00e9s par l'IA dans la revue Frontiers - un rat avec des testicules g\u00e9ants, rien de moins. C'\u00e9tait comique, mais cela montrait que l'examen par les pairs ne permettait pas toujours de d\u00e9celer les utilisations manifestement \u00e9videntes de l'IA.\u00a0<\/span><\/p>\n

    En fin de compte, comme tout outil, l'IA n'est s\u00fbre et efficace que dans la mesure o\u00f9 l'homme qui la met en \u0153uvre l'est aussi. Une utilisation imprudente, m\u00eame si elle n'est pas intentionnelle, peut \u00e9garer la science.<\/span><\/p>\n

    Les nouvelles lignes directrices visent \u00e0 pr\u00e9server l'honn\u00eatet\u00e9 des \"honn\u00eates gens\", selon les termes de M. Narayanan. <\/span><\/p>\n

    Une adoption g\u00e9n\u00e9ralis\u00e9e par les chercheurs, les \u00e9valuateurs et les revues pourrait \u00e9tablir une nouvelle norme d'int\u00e9grit\u00e9 scientifique \u00e0 l'\u00e8re de l'IA.<\/span><\/p>\n

    Toutefois, il sera difficile de parvenir \u00e0 un consensus, d'autant plus que la crise de la reproductibilit\u00e9 passe d\u00e9j\u00e0 inaper\u00e7ue.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    L'IA transforme la recherche scientifique, mais si elle n'est pas correctement encadr\u00e9e, elle risque de faire plus de mal que de bien. Telle est la conclusion d'un nouvel article publi\u00e9 dans Science Advances par une \u00e9quipe interdisciplinaire de 19 chercheurs dirig\u00e9e par les informaticiens Arvind Narayanan et Sayash Kapoor de l'universit\u00e9 de Princeton. L'\u00e9quipe affirme que l'utilisation abusive de l'apprentissage automatique dans les disciplines scientifiques alimente une crise de reproductibilit\u00e9 qui menace de saper les fondements m\u00eames de la science. \"Lorsque l'on passe des m\u00e9thodes statistiques traditionnelles aux m\u00e9thodes d'apprentissage automatique, les possibilit\u00e9s de se tirer une balle dans le pied sont beaucoup plus nombreuses\", a d\u00e9clar\u00e9 Narayanan, qui a particip\u00e9 \u00e0 la r\u00e9daction de l'article.<\/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\/fr\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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\/fr\/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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