{"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\/pt\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Equipa liderada por Princeton d\u00e1 o alarme: A IA apresenta riscos para a integridade cient\u00edfica"},"content":{"rendered":"

A IA est\u00e1 a transformar a investiga\u00e7\u00e3o cient\u00edfica, mas sem uma orienta\u00e7\u00e3o adequada, pode fazer mais mal do que bem.<\/strong><\/p>\n

Esta \u00e9 a conclus\u00e3o apontada por um <\/span>novo documento<\/span><\/a> publicada na revista Science Advances por uma equipa interdisciplinar de 19 investigadores liderada pelos cientistas inform\u00e1ticos da Universidade de Princeton Arvind Narayanan e Sayash Kapoor.\u00a0<\/span><\/p>\n

A equipa argumenta que a utiliza\u00e7\u00e3o indevida da aprendizagem autom\u00e1tica nas disciplinas cient\u00edficas est\u00e1 a alimentar uma crise de reprodutibilidade que amea\u00e7a minar os pr\u00f3prios fundamentos da ci\u00eancia.<\/span><\/p>\n

\"Quando passamos dos m\u00e9todos estat\u00edsticos tradicionais para os m\u00e9todos de aprendizagem autom\u00e1tica, h\u00e1 um n\u00famero muito maior de formas de dar um tiro no p\u00e9\". <\/span>afirmou Narayanan<\/span><\/a>que dirige o Centro de Pol\u00edtica de Tecnologia da Informa\u00e7\u00e3o de Princeton.\u00a0<\/span><\/p>\n

\"Se n\u00e3o houver uma interven\u00e7\u00e3o para melhorar as nossas normas cient\u00edficas e as normas de comunica\u00e7\u00e3o no que diz respeito \u00e0 ci\u00eancia baseada na aprendizagem autom\u00e1tica, arriscamo-nos a que n\u00e3o seja apenas uma disciplina, mas muitas disciplinas cient\u00edficas diferentes voltem a descobrir estas crises, uma ap\u00f3s outra\".<\/span><\/p>\n

Segundo os autores, o problema reside no facto de a aprendizagem autom\u00e1tica ter sido rapidamente adoptada por quase todos os dom\u00ednios cient\u00edficos, muitas vezes sem normas claras que garantam a integridade e a reprodutibilidade dos resultados. <\/span><\/p>\n

Salientam que t<\/span>J\u00e1 foram publicados milhares de artigos que utilizam m\u00e9todos de aprendizagem autom\u00e1tica incorrectos.<\/span><\/p>\n

Mas a equipa liderada por Princeton afirma que ainda h\u00e1 tempo para evitar esta crise iminente. Apresentaram uma lista de verifica\u00e7\u00e3o simples de boas pr\u00e1ticas que, se forem amplamente adoptadas, poder\u00e3o salvaguardar a fiabilidade da aprendizagem autom\u00e1tica na ci\u00eancia.\u00a0<\/span><\/p>\n

A lista de controlo, denominada REFORMS (Recommendations for Machine-learning-based Science), \u00e9 composta por 32 perguntas em oito \u00e1reas-chave:<\/span><\/p>\n

    \n
  1. Objectivos do estudo<\/b>: Indicar claramente a afirma\u00e7\u00e3o cient\u00edfica que est\u00e1 a ser feita e a forma como a aprendizagem autom\u00e1tica ser\u00e1 utilizada para a apoiar. Justificar a escolha da aprendizagem autom\u00e1tica em vez dos m\u00e9todos estat\u00edsticos tradicionais.<\/span><\/li>\n
  2. Reprodutibilidade computacional<\/b>: Fornecer o c\u00f3digo, os dados, as especifica\u00e7\u00f5es do ambiente de computa\u00e7\u00e3o, a documenta\u00e7\u00e3o e um gui\u00e3o de reprodu\u00e7\u00e3o necess\u00e1rios para que outros possam reproduzir os resultados do estudo de forma independente.<\/span><\/li>\n
  3. Qualidade dos dados<\/b>: Documentar as fontes de dados, a base de amostragem, as vari\u00e1veis de resultados, a dimens\u00e3o da amostra e a quantidade de dados em falta. Justificar que o conjunto de dados \u00e9 adequado e representativo da quest\u00e3o cient\u00edfica.<\/span><\/li>\n
  4. Pr\u00e9-processamento de dados<\/b>: Informe como os dados foram limpos, transformados e divididos em conjuntos de treino e de teste. Forne\u00e7a uma justifica\u00e7\u00e3o para quaisquer dados que tenham sido exclu\u00eddos.<\/span><\/li>\n
  5. Modela\u00e7\u00e3o<\/b>: Descrever e justificar todos os modelos experimentados, o m\u00e9todo utilizado para selecionar o(s) modelo(s) final(is) e o processo de afina\u00e7\u00e3o dos hiperpar\u00e2metros. Comparar o desempenho com as linhas de base adequadas.<\/span><\/li>\n
  6. Fuga de dados<\/b>: Verificar se o processo de modela\u00e7\u00e3o n\u00e3o utilizou inadvertidamente informa\u00e7\u00f5es provenientes dos dados de teste e se as caracter\u00edsticas de entrada n\u00e3o d\u00e3o origem a fugas no resultado.<\/span><\/li>\n<\/ol>\n

    \"Este \u00e9 um problema sistem\u00e1tico com solu\u00e7\u00f5es sistem\u00e1ticas\", explica Kapoor. <\/span><\/p>\n

    No entanto, os custos de um erro<\/span> poder\u00e1 ser imenso. Uma ci\u00eancia deficiente pode afundar a investiga\u00e7\u00e3o promissora, desencorajar os investigadores e corroer a confian\u00e7a do p\u00fablico na ci\u00eancia.\u00a0<\/span><\/p>\n

    Investiga\u00e7\u00f5es anteriores, tais como <\/span>Inqu\u00e9rito em grande escala da natureza<\/span><\/a> de acad\u00e9micos sobre a IA generativa na ci\u00eancia, indicou que a integra\u00e7\u00e3o mais profunda e progressiva da IA nos fluxos de trabalho cient\u00edficos \u00e9 inevit\u00e1vel. <\/span><\/p>\n

    Os participantes destacaram muitos benef\u00edcios - 66% observaram que a IA permite um processamento de dados mais r\u00e1pido, 58% acreditam que melhora os c\u00e1lculos e 55% afirmaram que poupa tempo e dinheiro.<\/span>\u00a0<\/span><\/p>\n

    No entanto, 53% consideraram que os resultados poderiam n\u00e3o ser reproduz\u00edveis, 58% preocuparam-se com a parcialidade e 55% consideraram que a IA poderia permitir investiga\u00e7\u00e3o fraudulenta.\u00a0<\/span><\/p>\n

    Observ\u00e1mos provas deste facto quando os investigadores <\/span>publicou um artigo<\/span><\/a> com diagramas sem sentido gerados por IA na revista Frontiers - um rato com test\u00edculos gigantes, nada menos. Foi c\u00f3mico, mas mostrou como a revis\u00e3o por pares pode nem sequer detetar utiliza\u00e7\u00f5es de IA claramente \u00f3bvias.\u00a0<\/span><\/p>\n

    Em \u00faltima an\u00e1lise, como qualquer ferramenta, a IA s\u00f3 \u00e9 t\u00e3o segura e eficaz quanto o ser humano que a utiliza. Uma utiliza\u00e7\u00e3o descuidada, mesmo que n\u00e3o intencional, pode desviar a ci\u00eancia do seu objetivo.<\/span><\/p>\n

    As novas directrizes t\u00eam como objetivo manter \"as pessoas honestas honestas\", como disse Narayanan. <\/span><\/p>\n

    A ado\u00e7\u00e3o generalizada por investigadores, revisores e revistas poder\u00e1 estabelecer um novo padr\u00e3o de integridade cient\u00edfica na era da IA.<\/span><\/p>\n

    No entanto, a constru\u00e7\u00e3o de um consenso ser\u00e1 um desafio, especialmente porque a crise da reprodutibilidade j\u00e1 est\u00e1 a passar despercebida.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    A IA est\u00e1 a transformar a investiga\u00e7\u00e3o cient\u00edfica, mas sem uma orienta\u00e7\u00e3o adequada, pode fazer mais mal do que bem. \u00c9 esta a conclus\u00e3o de um novo artigo publicado na revista Science Advances por uma equipa interdisciplinar de 19 investigadores liderada pelos cientistas inform\u00e1ticos da Universidade de Princeton, Arvind Narayanan e Sayash Kapoor. A equipa argumenta que a m\u00e1 utiliza\u00e7\u00e3o da aprendizagem autom\u00e1tica nas disciplinas cient\u00edficas est\u00e1 a alimentar uma crise de reprodutibilidade que amea\u00e7a minar os pr\u00f3prios fundamentos da ci\u00eancia. \"Quando passamos dos m\u00e9todos estat\u00edsticos tradicionais para os m\u00e9todos de aprendizagem autom\u00e1tica, h\u00e1 um n\u00famero muito maior de formas de dar um tiro no p\u00e9\", afirmou Narayanan, que<\/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\/pt\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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\/pt\/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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