Gran parte del entorno en el que nos movemos está total o parcialmente afectado por la inteligencia artificial y los modelos que se desarrollan a partir de ella. Desde las recomendaciones personalizadas que nos hacen las plataformas en streaming hasta la biometría de los smartphones, pasando por el tiempo de espera que aparece en la parada del autobús o los anuncios de Google en función de nuestro carrito de la compra, se trata de una tecnología que ha impregnado nuestras vidas. Y como tal, tiene sus beneficios y sus posibles riesgos para la sociedad.
La Comisión Europea es consciente de esta situación y se ha propuesto actuar para aprovechar al máximo las oportunidades que la IA nos brinda. Por ello, el pasado 21 de abril presentó la primera legislación sobre Inteligencia Artificial, delimitando un marco legal para abordar los riesgos y sesgos de la IA en aras de “garantizar la seguridad y los derechos fundamentales de los usuarios, a fin de que haya confianza en el desarrollo y la adopción de la IA”.
Este reglamento llega para apoyar un concepto en auge y cada vez más demandado, tanto por las compañías como por la sociedad. Se trata de la llamada inteligencia artificial explicable (Explainable AI o XAI), directamente relacionada con la ética, transparencia y confiabilidad de los modelos generados.
La IA explicable es aquella inteligencia que las personas podemos entender e interpretar y, por ende, en la que podemos confiar. Es un concepto que surge por el impacto que pueden tener las interacciones que se realizan con las personas, a modo de mecanismo de control para garantizar que dichas interacciones sean éticas, morales y transparentes.
Aplicado a los modelos de IA, los que incorporan la IA explicable son aquellos que tratan de evitar los sesgos. Estos sesgos aparecen al elaborar y entrenar los modelos, es decir, al escoger la muestra y nutrirlos de datos para el análisis, de manera que aquellos basados en IA explicable son los que buscan evitar situaciones y resultados sesgados donde la IA pueda impactar de forma negativa en las personas.
“Cuando una compañía o un ingeniero está desarrollando un modelo de IA, machine learning o deep learning, es vital que los datos utilizados no se encuentren sesgados. Si lo están, el modelo, sus resultados y la interacción con el cliente o usuario también lo estará, pudiendo acarrear perjuicios que afectan a las personas”, explica Jesús Aguilera, Artificial Intelligence & Analytics Presales Manager de SAS Iberia. “Desde SAS estamos comprometidos con la IA explicable y llevamos décadas desplegando herramientas de control que ayudan a los desarrolladores a crear desde la ética y la responsabilidad, de manera que se garantice en todo momento la integridad de los datos y la información, generando una relación de confianza básica y esencial entre la tecnología, las compañías y la sociedad.”, añade.
Para Silvina Arce, vicepresidenta y CFO de Club CDO Spain, “la tensión entre la ética y la transformación digital, asociada a una parte más de negocio, es difícil de equilibrar, pero es algo prioritario para todos los agentes públicos y privados implicados en la IA. Garantizar la confianza de los usuarios en los modelos es fundamental para que la IA se utilice de forma adecuada y se pueda extraer todo su potencial – y por ende el de los datos – para el crecimiento empresarial y social”.
En este sentido, existen tres palancas fundamentales que ayudan a poner freno a las decisiones automatizadas sesgadas:
Fuente: https://revistabyte.es/
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