{"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\/es\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Un equipo de Princeton da la voz de alarma: La IA pone en peligro la integridad cient\u00edfica"},"content":{"rendered":"

La IA est\u00e1 transformando la investigaci\u00f3n cient\u00edfica, pero sin la orientaci\u00f3n adecuada puede hacer m\u00e1s mal que bien.<\/strong><\/p>\n

Esta es la conclusi\u00f3n de un <\/span>nuevo papel<\/span><\/a> publicado en Science Advances por un equipo interdisciplinar de 19 investigadores dirigido por los inform\u00e1ticos de la Universidad de Princeton Arvind Narayanan y Sayash Kapoor.\u00a0<\/span><\/p>\n

El equipo sostiene que el uso indebido del aprendizaje autom\u00e1tico en todas las disciplinas cient\u00edficas est\u00e1 alimentando una crisis de reproducibilidad que amenaza con socavar los fundamentos mismos de la ciencia.<\/span><\/p>\n

\"Cuando pasamos de los m\u00e9todos estad\u00edsticos tradicionales a los m\u00e9todos de aprendizaje autom\u00e1tico, hay un n\u00famero mucho mayor de formas de pegarse un tiro en el pie\". <\/span>dijo Narayanan<\/span><\/a>que dirige el Centro de Pol\u00edtica Inform\u00e1tica de Princeton.\u00a0<\/span><\/p>\n

\"Si no tenemos una intervenci\u00f3n para mejorar nuestros est\u00e1ndares cient\u00edficos y los est\u00e1ndares de informaci\u00f3n cuando se trata de ciencia basada en el aprendizaje autom\u00e1tico, corremos el riesgo de que no solo una disciplina, sino muchas disciplinas cient\u00edficas diferentes redescubran estas crisis una tras otra.\"<\/span><\/p>\n

Seg\u00fan los autores, el problema es que el aprendizaje autom\u00e1tico se ha adoptado r\u00e1pidamente en casi todos los campos cient\u00edficos, a menudo sin normas claras que garanticen la integridad y reproducibilidad de los resultados. <\/span><\/p>\n

Destacan que t<\/span>Ya se han publicado miles de art\u00edculos que utilizan m\u00e9todos de aprendizaje autom\u00e1tico err\u00f3neos.<\/span><\/p>\n

Pero el equipo dirigido por Princeton afirma que a\u00fan se est\u00e1 a tiempo de evitar esta crisis inminente. Han presentado una sencilla lista de buenas pr\u00e1cticas que, si se adoptan de forma generalizada, podr\u00edan salvaguardar la fiabilidad del aprendizaje autom\u00e1tico en la ciencia.\u00a0<\/span><\/p>\n

La lista de comprobaci\u00f3n, denominada REFORMS (Recommendations for Machine-learning-based Science), consta de 32 preguntas en ocho \u00e1reas clave:<\/span><\/p>\n

    \n
  1. Objetivos del estudio<\/b>: Exponga claramente la afirmaci\u00f3n cient\u00edfica que se hace y c\u00f3mo se utilizar\u00e1 el aprendizaje autom\u00e1tico para respaldarla. Justifique la elecci\u00f3n del aprendizaje autom\u00e1tico frente a los m\u00e9todos estad\u00edsticos tradicionales.<\/span><\/li>\n
  2. Reproducibilidad computacional<\/b>: Proporcione el c\u00f3digo, los datos, las especificaciones del entorno inform\u00e1tico, la documentaci\u00f3n y un script de reproducci\u00f3n necesarios para que otras personas puedan reproducir los resultados del estudio de forma independiente.<\/span><\/li>\n
  3. Calidad de los datos<\/b>: Documente las fuentes de datos, el marco de muestreo, las variables de resultado, el tama\u00f1o de la muestra y la cantidad de datos que faltan. Justifique que el conjunto de datos es adecuado y representativo de la cuesti\u00f3n cient\u00edfica.<\/span><\/li>\n
  4. Preprocesamiento de datos<\/b>: Indique c\u00f3mo se limpiaron, transformaron y dividieron los datos en conjuntos de entrenamiento y de prueba. Explique por qu\u00e9 se excluyeron los datos.<\/span><\/li>\n
  5. Modelado<\/b>: Describir y justificar todos los modelos probados, el m\u00e9todo utilizado para seleccionar el modelo o modelos finales y el proceso de ajuste de los hiperpar\u00e1metros. Compare el rendimiento con las l\u00edneas de base adecuadas.<\/span><\/li>\n
  6. Fuga de datos<\/b>: Verificar que el proceso de modelado no haya utilizado inadvertidamente informaci\u00f3n de los datos de prueba y que las caracter\u00edsticas de entrada no filtren el resultado.<\/span><\/li>\n<\/ol>\n

    \"Se trata de un problema sistem\u00e1tico con soluciones sistem\u00e1ticas\", explica Kapoor. <\/span><\/p>\n

    Sin embargo, los costes de equivocarse<\/span> podr\u00eda ser inmensa. Una ciencia defectuosa podr\u00eda hundir investigaciones prometedoras, desanimar a los investigadores y erosionar la confianza p\u00fablica en la ciencia.\u00a0<\/span><\/p>\n

    Investigaciones anteriores, como <\/span>Estudio a gran escala de la naturaleza<\/span><\/a> de acad\u00e9micos sobre IA generativa en la ciencia, indic\u00f3 que la integraci\u00f3n m\u00e1s profunda y progresiva de la IA en los flujos de trabajo cient\u00edficos es inevitable. <\/span><\/p>\n

    Los participantes destacaron un mont\u00f3n de ventajas: 66% se\u00f1alaron que la IA permite un procesamiento de datos m\u00e1s r\u00e1pido, 58% cre\u00edan que mejora los c\u00e1lculos y 55% dijeron que ahorra tiempo y dinero.<\/span>\u00a0<\/span><\/p>\n

    Sin embargo, 53% consideraron que los resultados podr\u00edan no ser reproducibles, 58% se mostraron preocupados por la parcialidad y 55% creyeron que la IA podr\u00eda permitir investigaciones fraudulentas.\u00a0<\/span><\/p>\n

    Observamos pruebas de ello cuando los investigadores <\/span>public\u00f3 un art\u00edculo<\/span><\/a> con diagramas sin sentido generados por IA en la revista Frontiers: una rata con test\u00edculos gigantes, nada menos. Es c\u00f3mico, pero demuestra que la revisi\u00f3n por pares puede no detectar ni siquiera los usos m\u00e1s obvios de la IA.\u00a0<\/span><\/p>\n

    En \u00faltima instancia, como cualquier herramienta, la IA es tan segura y eficaz como el ser humano que la utiliza. Un uso descuidado, aunque no sea intencionado, puede llevar a la ciencia por mal camino.<\/span><\/p>\n

    Las nuevas directrices pretenden mantener \"honrada a la gente honrada\", en palabras de Narayanan. <\/span><\/p>\n

    La adopci\u00f3n generalizada por parte de investigadores, revisores y revistas podr\u00eda establecer una nueva norma de integridad cient\u00edfica en la era de la IA.<\/span><\/p>\n

    Sin embargo, llegar a un consenso ser\u00e1 dif\u00edcil, sobre todo porque la crisis de la reproducibilidad ya est\u00e1 pasando desapercibida.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    La IA est\u00e1 transformando la investigaci\u00f3n cient\u00edfica, pero sin la orientaci\u00f3n adecuada puede hacer m\u00e1s mal que bien. Esa es la aguda conclusi\u00f3n de un nuevo art\u00edculo publicado en Science Advances por un equipo interdisciplinar de 19 investigadores dirigido por los inform\u00e1ticos de la Universidad de Princeton Arvind Narayanan y Sayash Kapoor. El equipo sostiene que el uso indebido del aprendizaje autom\u00e1tico en todas las disciplinas cient\u00edficas est\u00e1 alimentando una crisis de reproducibilidad que amenaza con socavar los fundamentos mismos de la ciencia. \"Cuando pasamos de los m\u00e9todos estad\u00edsticos tradicionales a los de aprendizaje autom\u00e1tico, hay muchas m\u00e1s formas de dispararse en el pie\", afirma Narayanan.<\/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\/es\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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\/es\/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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