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border-top","layout":"SIMPLE","id":"container-d442aa671d",":items":{"text_894059747":{"id":"text-5564d5a73b","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003ETemas de gobernanza de la IA:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-b704353d95","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-compliance/\"\u003ECumplimiento de la IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-ethics/\"\u003EÉtica de la IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EGestión de riesgos de la IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ETrazabilidad de la IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003ETransparencia de la IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003ESesgo algorítmico\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/eu-ai-act/\"\u003ELey de IA de la UE\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO 42001\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-f61330d565","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":items":{"callout_what_is_ai_g":{"id":"text-51a9391a38","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDEFINICIÓN DE IA RESPONSABLE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa IA responsable es la práctica de diseñar, crear y operar sistemas de IA para que sean justos, transparentes, responsables, seguros, respetuosos con la privacidad y estén gobernados a lo largo de todo el ciclo de vida de la IA.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_intro_0":{"id":"text-776a511445","text":"\u003Cp\u003EA menudo existe una brecha entre lo que una organización dice sobre sus valores de IA y lo que puede demostrar sobre un sistema que se ejecuta en producción. La equidad, la transparencia y la responsabilidad son principios útiles, pero solo se hacen realidad cuando los equipos definen métricas de equidad, examinan los datos de entrenamiento, documentan el comportamiento del modelo, asignan derechos de decisión y supervisan la deriva del modelo después de la implementación.\u003C/p\u003E\n\u003Cp\u003EEse es el trabajo de la IA responsable. Transforma los principios generales en requisitos operativos: cómo se obtienen los datos, cómo se evalúan los modelos, quién revisa los resultados de alto riesgo, qué evidencias se conservan y cómo responden los equipos cuando un sistema se comporta de forma inesperada.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item":{"id":"quote-item-a35e55baff","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"The challenge with these new AI tools isn’t to identify useful applications of them. Recent history has demonstrated that users are eager to put them to use. For enterprise leaders, the real challenge is to figure out how to enable their use effectively, responsibly and at scale.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_intro_0_copy":{"id":"text-a5aa566497","text":"\u003Cp\u003EPara las empresas, la IA responsable se está convirtiendo en una disciplina práctica para crear, implementar y gobernar sistemas de IA diseñados para respaldar el escrutinio de reguladores, clientes, empleados y partes interesadas internas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sources_of_alg":{"id":"title-v2-53047951ba","additionalClasses":"anchor-title anchor-title--what-is-responsible-ai","type":"heading2","lines":["¿Qué es la IA responsable?"],":type":"snowflake-site/components/title-v2"},"text_what-is-algorithmic-bias_0":{"id":"text-7751149f62","text":"\u003Cp\u003ELa IA responsable es la práctica de diseñar, crear y operar sistemas de IA de acuerdo con principios éticos para que sean justos, transparentes, responsables, seguros y respetuosos con la privacidad. Está estrechamente relacionada con la IA ética, la IA fiable y la IA centrada en las personas, pero el énfasis es práctico: un programa de IA responsable convierte los principios en requisitos que los equipos pueden aplicar a lo largo del ciclo de vida de la IA.\u003C/p\u003E\n\u003Cp\u003EEl énfasis operativo significa que la IA responsable no funciona como una barrera que un sistema atraviesa antes del lanzamiento. Se aplica a todo el ciclo de vida: desde las decisiones sobre los datos de entrenamiento antes de crear un modelo, pasando por la evaluación, la implementación y la monitorización, hasta las revisiones de gobernanza que determinan si un sistema sigue ajustándose a su uso previsto. La IA responsable define lo que se considera correcto en cada una de esas fases, y la \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/\"\u003Egobernanza de la IA\u003C/a\u003E crea los roles, los controles y las rutas de auditoría que ayudan a la organización a aplicarla.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sources-of-algorithmic-bias":{"id":"title-v2-34555f64ec","additionalClasses":"anchor-title anchor-title--core-principles-of-responsible-ai","type":"heading2","lines":["Principios básicos de la IA responsable"],":type":"snowflake-site/components/title-v2"},"text_sources-of-algorithmic-bias_0":{"id":"text-4792af7d62","text":"\u003Cp\u003ELos principios que se indican a continuación suelen tratarse por separado, pero en producción interactúan entre sí. Una decisión de privacidad puede afectar a las pruebas de equidad, un requisito de transparencia puede cambiar la documentación del modelo y un modelo de responsabilidad puede determinar si un paso con intervención humana tiene autoridad real o solo una revisión nominal.\u003C/p\u003E\n\u003Ch3\u003EEquidad\u003C/h3\u003E\n\u003Cp\u003ELa equidad de la IA significa que los sistemas de IA no deben producir resultados discriminatorios en función de atributos protegidos u otros grupos relevantes. Uno de los riesgos más comunes es el \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Esesgo algorítmico\u003C/a\u003E: diferencias sistemáticas en el rendimiento de un modelo para distintas poblaciones, a menudo debido a datos históricos sesgados, variables proxy o una representación desigual en los datos de entrenamiento. La equidad requiere algo más que eliminar los campos sensibles de una tabla, porque las variables proxy pueden seguir apareciendo en los datos de ubicación, los patrones de ingresos, el comportamiento del dispositivo o las etiquetas históricas.\u003C/p\u003E\n\u003Cp\u003ELos equipos suelen examinar métricas de equidad como la paridad demográfica, la igualdad de oportunidades o las diferencias en las tasas de error, y después deciden qué métrica se ajusta al caso de uso y al perfil de riesgo. Un modelo de fraude, un modelo de préstamos y un modelo de triaje médico pueden requerir pruebas de equidad diferentes, porque las consecuencias de los falsos positivos y los falsos negativos no son las mismas.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica\u003C/b\u003E\u003Cspan\u003E\u003Cb\u003E: \u003C/b\u003Erealiza pruebas de equidad antes de la implementación y de forma periódica, documentando las métricas seleccionadas, los resultados por grupo, las limitaciones conocidas y las decisiones de mitigación.\u003C/span\u003E\u003C/p\u003E\n\u003Ch3\u003ETransparencia\u003C/h3\u003E\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etransparencia de la IA\u003C/a\u003E significa que las partes interesadas pueden entender qué hace un sistema de IA, por qué se creó, qué datos utiliza y en qué casos no se debe confiar en sus resultados. Para algunos sistemas, eso puede significar un comportamiento explicable del modelo, mientras que para otros puede significar una divulgación clara al usuario, una ficha de modelo, una ficha de datos o una descripción en lenguaje sencillo del uso previsto.\u003C/p\u003E\n\u003Cp\u003EEl objetivo no es exponer todos los parámetros del modelo a todos los usuarios. Se trata de dar a la audiencia adecuada el nivel de información adecuado: los ejecutivos necesitan conocer la posición de riesgo, los desarrolladores necesitan detalles de implementación, los revisores necesitan evidencias de la evaluación y los usuarios afectados pueden necesitar saber cuándo la IA ha contribuido a una decisión.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica: \u003C/b\u003Epublica una ficha de modelo o un registro del sistema equivalente que cubra el uso previsto, las fuentes de datos, los resultados de la evaluación, las limitaciones, la supervisión humana y las vías de escalado.\u003C/p\u003E\n\u003Ch3\u003EResponsabilidad\u003C/h3\u003E\n\u003Cp\u003ELa responsabilidad significa designar a las personas que son responsables de las decisiones en torno a un sistema de IA. Un modelo puede generar un resultado, pero no puede aprobar su propio uso, asumir riesgos normativos, explicar un incidente a un cliente ni decidir si debe corregirse un patrón perjudicial.\u003C/p\u003E\n\u003Cp\u003EUna responsabilidad clara suele incluir un patrocinador ejecutivo, un responsable de negocio, responsables técnicos, revisores legales o de riesgos y responsables operativos que puedan intervenir después de la implementación. Sin esa estructura, la IA responsable es difícil de aplicar: nadie tiene autoridad explícita para detener un modelo, cambiar una política de acceso, aprobar una excepción o exigir un reentrenamiento.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica:\u003C/b\u003E asigna propietarios responsables para cada caso de uso de IA, e indica quién aprueba la implementación, quién revisa los incidentes y quién puede suspender o revertir el sistema.\u003C/p\u003E\n\u003Ch3\u003EPrivacidad\u003C/h3\u003E\n\u003Cp\u003ELa privacidad significa que los sistemas de IA deben utilizar los datos de forma que respeten los derechos individuales, los compromisos contractuales y la finalidad original para la que se recopilaron los datos. Para los equipos de IA, esto suele empezar por la minimización de datos y la limitación de la finalidad: usar solo los campos necesarios para la tarea, con controles de consentimiento, conservación y acceso cuando sea necesario.\u003C/p\u003E\n\u003Cp\u003ELas técnicas de \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/machine-learning/\"\u003Eaprendizaje automático\u003C/a\u003E (ML) que preservan la privacidad pueden ayudar a reducir la exposición, pero no sustituyen la gobernanza de las fuentes de datos, las etiquetas, los prompts, las representaciones vectoriales, los registros y los resultados del modelo. Una revisión de IA responsable debe preguntarse si realmente se necesitan datos personales, si los datos sintéticos son adecuados, si se utilizan atributos sensibles para las pruebas o el entrenamiento y cómo se audita el acceso a lo largo del tiempo.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica:\u003C/b\u003E revisa los datos de entrenamiento, validación e inferencia en cuanto a limitación de la finalidad, consentimiento, atributos sensibles, reglas de conservación y controles de acceso antes de que el modelo entre en producción.\u003C/p\u003E\n\u003Ch3\u003ESeguridad\u003C/h3\u003E\n\u003Cp\u003ELa seguridad de la IA significa que un sistema de IA debe fallar de forma predecible y reversible. El sistema debe tener límites sobre lo que puede hacer, controles que reduzcan los resultados dañinos y una monitorización que ayude a los equipos a detectar comportamientos inseguros antes de que se conviertan en un incidente.\u003C/p\u003E\n\u003Cp\u003EEn la IA generativa y los sistemas de agentes de IA, la seguridad suele incluir ejercicios de equipo rojo (red teaming), límites de frecuencia, filtrado de contenido, restricciones en el uso de herramientas, planes de reversión y revisión humana para las acciones de alto riesgo. El sistema también debe tener una ruta definida para notificar problemas, porque el comportamiento inseguro puede deberse a la degradación del modelo, la inyección de prompts, cambios en los datos, usos malintencionados o un desajuste entre el uso previsto y el uso real.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica:\u003C/b\u003E somete el sistema a operaciones de equipo rojo antes del lanzamiento, define categorías de resultados inseguros, establece umbrales de escalado y mantén una ruta de reversión o desactivación.\u003C/p\u003E\n\u003Ch3\u003EInclusión\u003C/h3\u003E\n\u003Cp\u003ELa inclusión significa que perspectivas diversas dan forma al propósito del sistema, los datos de entrenamiento, la evaluación y el contexto de implementación. Esto incluye a las personas que crean el sistema, los equipos de negocio que lo utilizan y las comunidades o grupos de usuarios afectados por sus resultados.\u003C/p\u003E\n\u003Cp\u003EEl diseño participativo puede sacar a la luz riesgos que son fáciles de pasar por alto en una revisión puramente técnica. Una herramienta para la automatización de la asistencia puede cambiar la forma en que los clientes experimentan el escalado; un modelo sanitario puede comportarse de forma diferente entre poblaciones de pacientes; y una herramienta de IA en el lugar de trabajo puede afectar a empleados que nunca aceptaron ser evaluados por un modelo. La inclusión significa dar espacio a esas perspectivas antes de la implementación, no solo después de quejas o incidentes.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAcción práctica: \u003C/b\u003Eincluye a representantes de los grupos de usuarios afectados, partes interesadas del negocio, equipos de riesgos y propietarios técnicos en la revisión de los casos de uso, los criterios de evaluación y los ciclos de retroalimentación posteriores a la implementación.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EMira este debate con líderes de Microsoft y Snowflake sobre cómo crear e implementar la IA de forma responsable:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube":{"id":"embed-7299a6076d","youtubeVideoId":"dzKmato_364","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_detecting-and-measuring-algorithmic-bias":{"id":"title-v2-5865781fda","additionalClasses":"anchor-title anchor-title--why-responsible-ai-matters","type":"heading2","lines":["Por qué es importante la IA responsable"],":type":"snowflake-site/components/title-v2"},"text_detecting-and-measuring-algorithmic-bias_0":{"id":"text-66ed6a2911","text":"\u003Cp\u003ELa IA responsable es importante porque un sistema de IA que pasa a producción sin controles claros puede generar riesgos para la reputación, exposición legal, pérdida de confianza de los clientes, correcciones operativas y deuda de modelos. La \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/eu-ai-act/\"\u003ELey de IA de la UE\u003C/a\u003E crea obligaciones basadas en el riesgo para los sistemas de IA. Los sistemas de alto riesgo están sujetos a requisitos que abarcan la gestión de riesgos, la \u003Ca href=\"https://www.snowflake.com/es/data-governance/\"\u003Egobernanza de datos\u003C/a\u003E, la documentación técnica, el mantenimiento de registros, la transparencia, la supervisión humana, la precisión, la solidez y la ciberseguridad.\u003C/p\u003E\n\u003Cp\u003ELa IA responsable cuenta con sólidos argumentos de negocio: la gobernanza de la IA y de los datos está ligada a la reputación, a la confianza de los clientes y a que una organización pueda demostrar que gestiona la IA de forma segura. Los clientes y los equipos de adquisiciones tratan cada vez más la madurez de la gobernanza como un umbral de calificación, no como un elemento diferenciador.\u003C/p\u003E\n\u003Cp\u003ELos principales marcos de gobernanza de la IA reflejan la importancia de la IA responsable. El marco \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003EAI Risk Management Framework del NIST\u003C/a\u003E es voluntario, pero está diseñado para ayudar a las organizaciones a incorporar consideraciones de fiabilidad en el diseño, el desarrollo, el uso y la evaluación de los sistemas de IA. \u003Ca href=\"https://www.iso.org/es/norma/42001\" target=\"_blank\"\u003EISO/IEC 42001\u003C/a\u003E adopta un enfoque de sistema de gestión y ofrece a las organizaciones una forma estructurada de gestionar los riesgos y las oportunidades de la IA, incluidas las consideraciones éticas, la transparencia y la mejora continua.\u003C/p\u003E\n\u003Cp\u003EPara los equipos operativos, las prácticas de IA responsable crean puntos de control más tempranos para detectar datos sesgados, responsabilidades poco claras, documentación ausente, casos de uso no validados y una supervisión insuficiente, lo que ayuda a reducir la probabilidad de detectar el problema tras una reclamación de un cliente, un requerimiento normativo o un incidente en producción.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what_is_ai_g_294410134":{"id":"text-03c8bf9943","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EERROR HABITUAL\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EEvita centrarte en el rendimiento agregado del modelo y pasar por alto las diferencias entre grupos, las variables proxy o la representación desigual de los datos que pueden generar resultados injustos en producción.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_mitigating-algorithmic-bias":{"id":"title-v2-c4b846f884","additionalClasses":"anchor-title anchor-title--how-to-put-responsible-ai-into-practice","type":"heading2","lines":["Cómo poner en práctica la IA responsable"],":type":"snowflake-site/components/title-v2"},"text_mitigating-algorithmic-bias_0":{"id":"text-c07f1bdd8f","text":"\u003Cp\u003ELa IA responsable funciona mejor como una disciplina que abarca todo el ciclo de vida. Los controles deben acompañar al sistema desde la obtención de los datos hasta el desarrollo del modelo, el implementación, la monitorización y la revisión periódica.\u003C/p\u003E\n\u003Ch3\u003EDatos: gobernar los datos de entrada antes de empezar el entrenamiento\u003C/h3\u003E\n\u003Cp\u003EUn ciclo de vida de IA responsable comienza con los datos. Los equipos necesitan saber de dónde proceden los datos de entrenamiento, si se aplican consentimientos o límites contractuales, cómo se crearon las etiquetas, qué atributos pueden introducir sesgos y si los datos reflejan a la población a la que afectará el modelo.\u003C/p\u003E\n\u003Cp\u003ELa gobernanza de los datos de entrenamiento también debe abarcar los datos sintéticos. Los datos sintéticos pueden ayudar con la privacidad, las pruebas o el equilibrio de clases, pero igualmente requieren documentación: qué los generó, qué datos originales los conformaron, dónde es apropiado usarlos y qué patrones pueden haberse amplificado o perdido.\u003C/p\u003E\n\u003Cp\u003EEntre los artefactos útiles en esta fase se incluyen una ficha de datos, registros de linaje, notas sobre la calidad de las etiquetas, resultados de detección de sesgos, políticas de acceso a los datos y registros de aprobación para campos sensibles.\u003C/p\u003E\n\u003Ch3\u003EModelo: documentar el uso previsto y realizar pruebas más allá de la precisión\u003C/h3\u003E\n\u003Cp\u003EDurante el desarrollo del modelo, las prácticas de IA responsable pasan de la idoneidad de los datos al comportamiento del sistema. Una ficha de modelo debe recoger el uso previsto, los usos fuera de alcance, los datos de evaluación, los resultados de rendimiento, las pruebas de equidad, las pruebas de solidez y las limitaciones documentadas.\u003C/p\u003E\n\u003Cp\u003EEs aquí donde los equipos deben comprobar si el modelo se comporta de forma diferente entre grupos, gestiona de manera fiable las variaciones habituales de las entradas de datos y ofrece puntuaciones de confianza o explicaciones que los revisores puedan interpretar. En los casos de uso de alto riesgo, el estándar de aprobación debe incluir pruebas de que el modelo es adecuado para el contexto de decisión, y no solo de que obtiene buenos resultados en un indicador de referencia agregado.\u003C/p\u003E\n\u003Ch3\u003EImplementación: situar los controles donde se toman las decisiones\u003C/h3\u003E\n\u003Cp\u003ELos sistemas de alto riesgo deben incluir una revisión humana con autoridad real, no un paso de aprobación superficial después de que el sistema ya haya tomado la decisión en la práctica.\u003C/p\u003E\n\u003Cp\u003ELos equipos también deben definir límites de frecuencia, controles de acceso, restricciones de salida, monitorización de la degradación del modelo, monitorización de sesgos y umbrales de escalado. En el caso de las aplicaciones de IA generativa, los controles de implementación pueden incluir restricciones de recuperación, protecciones contra la inyección de prompts, moderación de los resultados, registro y restricciones en las llamadas a herramientas o las acciones posteriores.\u003C/p\u003E\n\u003Ch3\u003EGobernanza: revisar, recertificar y responder\u003C/h3\u003E\n\u003Cp\u003EUn comité de revisión de IA responsable u órgano de gobernanza equivalente debe evaluar los casos de uso de mayor riesgo, aprobar excepciones, revisar incidentes y exigir una recertificación periódica.\u003C/p\u003E\n\u003Cp\u003EEl proceso de revisión debe generar pruebas que la organización pueda consultar más adelante, como decisiones de aprobación, fichas de modelos, fichas de datos, informes de incidentes de IA, excepciones a las políticas, resultados de monitorización y planes de corrección. A medida que los sistemas cambian, estos registros ayudan a los equipos a entender si un modelo sigue ajustándose a su uso previsto, si la degradación ha modificado los resultados y si las nuevas normativas o políticas internas exigen controles adicionales.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_regulatory-expectations-on-algorithmic-bias":{"id":"title-v2-f9e0c8e548","additionalClasses":"anchor-title anchor-title--responsible-ai-vs-ai-ethics-vs-ai-governance","type":"heading2","lines":["IA responsable, ética de la IA y gobernanza de la IA"],":type":"snowflake-site/components/title-v2"},"text_regulatory-expectations-on-algorithmic-bias_0":{"id":"text-a8f24b1110","text":"\u003Cp\u003EEstos términos se solapan, pero no son intercambiables.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-ethics/\"\u003Eética de la IA\u003C/a\u003E aborda las cuestiones normativas: ¿qué debería permitirse hacer a un sistema de IA? ¿Qué usos son inaceptables? ¿Qué obligaciones tiene una organización con los usuarios, los clientes, los empleados y las comunidades afectadas?\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELa IA responsable transforma esos valores en principios operativos (equidad, transparencia, responsabilidad, privacidad, seguridad e inclusión) y pide a los equipos que los apliquen mediante decisiones de diseño, documentación, pruebas, supervisión y monitorización.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELa gobernanza de la IA hace que esos principios sean exigibles. Asigna derechos de decisión, crea procesos de revisión, vincula controles a los datos y los modelos, hace un seguimiento de las aprobaciones, monitoriza los sistemas de producción y registra pruebas para auditorías, cumplimiento y respuesta a incidentes.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELa ética de la IA plantea la pregunta. La IA responsable define los principios operativos. La gobernanza de la IA crea el sistema empresarial que los hace cumplir.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-snowflake-helps-organizations-address-algorithmic-bias":{"id":"title-v2-7fe9adeb2b","additionalClasses":"anchor-title anchor-title--responsible-ai-on-snowflake","type":"heading2","lines":["IA responsable en Snowflake"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-helps-organizations-address-algorithmic-bias_0":{"id":"text-b595ace765","text":"\u003Cp\u003ELa IA responsable depende del contexto: los datos utilizados para entrenar o fundamentar un modelo, las políticas de acceso que determinan quién puede utilizarlo, el linaje que muestra de dónde proceden las entradas, la monitorización que detecta comportamientos inesperados y los controles que moderan los resultados en tiempo de ejecución. \u003Ca href=\"https://www.snowflake.com/es/product/platform/\"\u003ESnowflake\u003C/a\u003E respalda la gobernanza de datos e IA en el mismo entorno en el que las organizaciones gestionan datos, aplicaciones y workloads de IA.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/es/blog/ISO-IEC-42001-AI-certification/\"\u003ESnowflake ha obtenido la certificación ISO/IEC 42001\u003C/a\u003E tras una auditoría independiente realizada por un tercero, que cubre los requisitos de su sistema de gestión de IA para establecer, implementar, mantener y mejorar continuamente la gobernanza de las tecnologías de IA.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E unifica la gobernanza de datos, aplicaciones y modelos mediante el control de acceso basado en roles (RBAC), el control de acceso basado en atributos (ABAC), la clasificación, el etiquetado, la monitorización y el linaje. El historial de acceso, Time Travel, los metadatos compartidos y el linaje en Snowflake y en el almacenamiento externo proporcionan a los equipos el contexto necesario para revisar cómo se utilizan los datos y cómo se aplican los controles a lo largo del tiempo.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-ai-guardrails\"\u003ESnowflake Cortex Guard\u003C/a\u003E está diseñado para filtrar respuestas de LLM potencialmente inseguras en tiempo de ejecución, de modo que evalúa los resultados del modelo antes de que se devuelvan a la aplicación.\u003C/p\u003E\n\u003Cp\u003EEn conjunto, estos controles ayudan a conectar los principios de la IA responsable con pruebas operativas, como quién accedió a los datos, qué políticas se aplicaron, qué muestra el linaje, dónde se moderaron los resultados y cómo se gobernaron los sistemas de IA dentro del patrimonio de datos general.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bias-control-starts-before-the-model":{"id":"title-v2-970b83eb27","additionalClasses":"anchor-title anchor-title--the-demand-for-responsible-ai","type":"heading2","lines":["La demanda de IA responsable"],":type":"snowflake-site/components/title-v2"},"text_bias-control-starts-before-the-model_0":{"id":"text-db37857d1f","text":"\u003Cp\u003ELas expectativas de los clientes y los partners en torno a la IA responsable están aumentando incluso más rápido que la mayoría de los plazos normativos. Las organizaciones que antes trataban la gobernanza de la IA como un ejercicio de cumplimiento están descubriendo que los equipos de adquisiciones, los clientes corporativos y las partes interesadas a nivel directivo hacen preguntas más específicas sobre prácticas de datos, documentación de los modelos, pruebas de equidad y respuesta a incidentes antes de firmar. \u003C/p\u003E\n\u003Cp\u003ELos equipos más avanzados suelen contar con datos de entrenamiento que pueden justificar, un comportamiento del modelo que han probado en distintos grupos, controles de implementación con autoridad real detrás y revisiones de gobernanza que producen pruebas en lugar de simples aprobaciones. Esos artefactos (las fichas de datos, las fichas de modelos, los informes de incidentes y los registros de auditoría) muestran cómo es la IA responsable en la práctica. Asimismo, constituyen la prueba para los reguladores, los clientes y las partes interesadas internas de que los valores declarados de una organización se materializan en sus sistemas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what_is_ai_g_1939414635":{"id":"text-ae25eb42e9","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECONCLUSIÓN CLAVE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa IA responsable transforma los principios de alto nivel en requisitos prácticos, lo que ayuda a los equipos a demostrar que los sistemas de IA se prueban, documentan, monitorizan y gobiernan de forma que respaldan la confianza y la revisión.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout_what_is_ai_g","text_intro_0","quote_item","text_intro_0_copy","title_sources_of_alg","text_what-is-algorithmic-bias_0","title_sources-of-algorithmic-bias","text_sources-of-algorithmic-bias_0","youtube","title_detecting-and-measuring-algorithmic-bias","text_detecting-and-measuring-algorithmic-bias_0","callout_what_is_ai_g_294410134","title_mitigating-algorithmic-bias","text_mitigating-algorithmic-bias_0","title_regulatory-expectations-on-algorithmic-bias","text_regulatory-expectations-on-algorithmic-bias_0","title_how-snowflake-helps-organizations-address-algorithmic-bias","text_how-snowflake-helps-organizations-address-algorithmic-bias_0","title_bias-control-starts-before-the-model","text_bias-control-starts-before-the-model_0","callout_what_is_ai_g_1939414635"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-4f60462738",":items":{"text_943981956_copy_":{"id":"text-9b385f02b9","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EEN ESTA GUÍA\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-fa787d77b8","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-responsible-ai\"\u003E¿Qué es la IA responsable?\u003C/li\u003E\n\u003Cli data-anchor=\"core-principles-of-responsible-ai\"\u003EPrincipios básicos de la IA responsable\u003C/li\u003E\n\u003Cli data-anchor=\"why-responsible-ai-matters\"\u003EPor qué es importante la IA responsable\u003C/li\u003E\n\u003Cli data-anchor=\"how-to-put-responsible-ai-into-practice\"\u003ECómo poner en práctica la IA responsable\u003C/li\u003E\n\u003Cli data-anchor=\"responsible-ai-vs-ai-ethics-vs-ai-governance\"\u003EIA responsable, ética de la IA y gobernanza de la IA\u003C/li\u003E\n\u003Cli data-anchor=\"responsible-ai-on-snowflake\"\u003EIA responsable en Snowflake\u003C/li\u003E\n\u003Cli data-anchor=\"the-demand-for-responsible-ai\"\u003ELa demanda de IA responsable\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_963294944":{"id":"flexible-column-container-0511294be6","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":items":{"title_v2_copy":{"id":"title-v2-010415d4ee","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Preguntas frecuentes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-5326fa5a93","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELas preguntas más frecuentes sobre la IA responsable, respondidas por expertos de Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-ccc43e0cd0","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"¿Es legalmente obligatoria la IA responsable?","richText":"\u003Cp\u003ELa IA responsable no se menciona como un requisito legal único, pero las principales normativas y estándares de IA codifican los principios de la IA responsable como obligaciones exigibles. 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