{"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\/es\/2023\/10\/new-research-into-datasets-reveals-systemic-ethical-and-legal-issues\/","title":{"rendered":"Una nueva investigaci\u00f3n sobre conjuntos de datos revela problemas \u00e9ticos y jur\u00eddicos sist\u00e9micos"},"content":{"rendered":"

La IA gira en torno a los datos, pero \u00bfde d\u00f3nde proceden? \u00bfSon legales y \u00e9ticos los conjuntos de datos? \u00bfC\u00f3mo lo determinan con seguridad los desarrolladores?\u00a0<\/b><\/p>\n

El entrenamiento de modelos de aprendizaje autom\u00e1tico, como los grandes modelos ling\u00fc\u00edsticos (LLM), requiere grandes vol\u00famenes de datos de texto.\u00a0<\/span><\/p>\n

Hay montones de conjuntos de datos disponibles en plataformas como Kaggle, GitHub y Hugging Face, pero se encuentran en una zona gris desde el punto de vista legal y \u00e9tico, principalmente por cuestiones de licencias y uso justo.\u00a0<\/span><\/p>\n

En Iniciativa para la Procedencia de los Datos<\/a>un esfuerzo de colaboraci\u00f3n entre investigadores de IA y profesionales del Derecho, revis\u00f3 miles de conjuntos de datos para arrojar luz sobre sus verdaderos or\u00edgenes. <\/span><\/p>\n

En <\/span>se centr\u00f3 en m\u00e1s de 1.800 conjuntos de datos disponibles en plataformas como Hugging Face, GitHub y Papers With Code. <\/span>Los conjuntos de datos est\u00e1n dise\u00f1ados principalmente para afinar modelos de c\u00f3digo abierto como Llama-2.\u00a0<\/span><\/p>\n

El estudio revel\u00f3 que aproximadamente 70% de estos conjuntos de datos carec\u00edan de informaci\u00f3n clara sobre licencias o estaban etiquetados con licencias excesivamente permisivas.\u00a0<\/span><\/p>\n

Con una flagrante falta de claridad sobre los derechos de autor y las restricciones de uso comercial, los desarrolladores de IA corren el riesgo de infringir accidentalmente la ley o violar los derechos de autor.<\/span><\/p>\n

Shayne Longpre, doctorando del MIT Media Lab que encabez\u00f3 la auditor\u00eda, subray\u00f3 que el problema no es culpa de las plataformas de alojamiento, sino de un problema sist\u00e9mico de la comunidad de aprendizaje autom\u00e1tico.<\/span><\/p>\n

2023 ha sido testigo de un aluvi\u00f3n de demandas<\/a> dirigidas a los principales desarrolladores de IA, como Meta, Anthropic y OpenAI, que est\u00e1n sometidos a una presi\u00f3n extrema para que adopten pr\u00e1cticas de recopilaci\u00f3n de datos m\u00e1s transparentes. Las normativas, como la Ley de IA de la UE<\/a>est\u00e1n preparados para hacer cumplir precisamente eso.\u00a0<\/span><\/p>\n

La Data Provenance Initiative permite a los desarrolladores de aprendizaje autom\u00e1tico <\/span>explore aqu\u00ed los conjuntos de datos auditados<\/span><\/a>. La iniciativa tambi\u00e9n analiza patrones dentro de los conjuntos de datos, arrojando luz sobre sus or\u00edgenes geogr\u00e1ficos e institucionales.\u00a0<\/span><\/p>\n

La mayor\u00eda de los conjuntos de datos se elaboran en el Norte Global angl\u00f3fono, lo que pone de relieve los desequilibrios socioculturales.\u00a0<\/span><\/p>\n

\"Procedencia
La Data Provenance Initiative descubri\u00f3 que los conjuntos de datos representan predominantemente a pa\u00edses angl\u00f3fonos y del Norte Global. Fuente: Datos Provenance.org<\/a>.<\/figcaption><\/figure>\n

M\u00e1s informaci\u00f3n sobre el estudio<\/span><\/h2>\n

Este an\u00e1lisis a gran escala de conjuntos de datos puso de manifiesto problemas sistem\u00e1ticos en la forma de recopilar y distribuir los datos. La iniciativa tambi\u00e9n elabor\u00f3 un documento para explicar sus conclusiones, <\/span>publicado aqu\u00ed<\/span><\/a>.<\/span><\/p>\n

Aqu\u00ed encontrar\u00e1 m\u00e1s informaci\u00f3n sobre los m\u00e9todos y resultados del estudio:<\/span><\/p>\n

    \n
  1. An\u00e1lisis de conjuntos de datos para determinar su origen y etiquetado<\/b>: Este estudio audit\u00f3 sistem\u00e1ticamente m\u00e1s de 1.800 conjuntos de datos de ajuste fino para examinar su procedencia, licencia y documentaci\u00f3n.\u00a0<\/span><\/li>\n
  2. Pruebas de etiquetado incorrecto<\/b>: Los resultados pusieron de manifiesto las diferencias entre los tipos de datos disponibles con distintas licencias y sus implicaciones para las interpretaciones jur\u00eddicas de los derechos de autor y el uso leal. Se descubri\u00f3 un alto \u00edndice de categorizaci\u00f3n err\u00f3nea de licencias, con m\u00e1s de 72% de conjuntos de datos sin especificar una licencia y un \u00edndice de error de 50% en los que s\u00ed lo hac\u00edan.<\/span><\/li>\n
  3. Procedencia poco fiable de los datos<\/b>: La investigaci\u00f3n llama la atenci\u00f3n sobre el problema de la procedencia poco fiable de los datos, haciendo hincapi\u00e9 en la necesidad de normas para rastrear el linaje de los datos, garantizar una atribuci\u00f3n adecuada y fomentar un uso responsable de los mismos.\u00a0<\/span><\/li>\n
  4. Distribuci\u00f3n geogr\u00e1fica: <\/b>El estudio pone de relieve una grave falta de representaci\u00f3n y atribuci\u00f3n de los conjuntos de datos procedentes del Sur Global. La mayor\u00eda de los conjuntos de datos giran en torno a la lengua inglesa y est\u00e1n culturalmente vinculados a Europa, Norteam\u00e9rica y Ocean\u00eda angl\u00f3fona.\u00a0<\/span><\/li>\n<\/ol>\n

    Este estudio pone de relieve problemas sist\u00e9micos y estructurales en la forma en que se crean, distribuyen y utilizan los datos. Los datos son un recurso esencial para la IA y, al igual que los recursos naturales, son finitos.\u00a0<\/span><\/p>\n

    Existe la preocupaci\u00f3n de que la tecnolog\u00eda de IA acabe superando los conjuntos de datos actuales e incluso <\/span>empezar a consumir su propia producci\u00f3n<\/span><\/a>lo que significa que los modelos de IA aprender\u00e1n del texto generado por la IA.\u00a0<\/span><\/p>\n

    Esto podr\u00eda erosionar la calidad de los modelos, lo que significa que los datos de alta calidad, \u00e9ticos y legales podr\u00edan llegar a ser muy valiosos. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    La IA gira en torno a los datos, pero \u00bfde d\u00f3nde proceden? \u00bfSon legales y \u00e9ticos los conjuntos de datos? \u00bfC\u00f3mo pueden determinarlo con seguridad los desarrolladores? El entrenamiento de modelos de aprendizaje autom\u00e1tico, como los grandes modelos ling\u00fc\u00edsticos (LLM), requiere grandes vol\u00famenes de datos de texto. Hay montones de conjuntos de datos disponibles en plataformas como Kaggle, GitHub y Hugging Face, pero se encuentran en una zona gris desde el punto de vista legal y \u00e9tico, principalmente debido a problemas de licencias y uso justo. La Data Provenance Initiative, un esfuerzo de colaboraci\u00f3n entre investigadores de IA y profesionales del derecho, revis\u00f3 miles de conjuntos de datos para arrojar luz sobre sus verdaderos or\u00edgenes. Se centr\u00f3 en m\u00e1s 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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