¿Quién enseña a la IA? | Ananya Vinod | TEDxSt Pauls College

La forma en que aprende la inteligencia artificial depende de los datos que recibe y de las decisiones humanas que hay detrás de ellos. Esta charla TEDx de Ananya Vinod analiza a las personas que entrenan los sistemas de IA y el papel de los valores humanos, los sesgos, la ética y la rendición de cuentas en su desarrollo. También reflexiona sobre cómo la IA influye en la manera en que aprendemos, trabajamos y vivimos.

• Cómo los sistemas de IA adquieren conocimiento a partir de datos
• Decisiones humanas, valores y sesgos en el entrenamiento de la IA
• Ética y rendición de cuentas en el desarrollo de la IA

The talk “Who Is Teaching AI?” explores how artificial intelligence learns, the data and human decisions that shape its knowledge, and the people responsible for training these systems. It examines the role of human values, biases, ethics, and accountability in AI development, encouraging audiences to think critically about how AI is taught and how it influences the way we learn, work, and live. Ananya Vinod is a Full Stack Developer at A.P. Moller–Maersk, AI Engineer, researcher, motivational speaker, and award-winning public speaker passionate about technology and human impact. At 19, she earned a place on the *Kaun Banega Crorepati* hot seat after outperforming millions of aspirants across India. She represented Bangalore Urban as a Youth Parliament Representative at Vidhana Soudha. A legal patent holder in Natural Language Processing, she has authored a Springer-published book chapter and IEEE publications. A National Champion of Speak for India, she has won numerous debate and public speaking championships and was named Best Delegate at the International Model United Nations in South Africa. At 21, she takes the TEDx stage to inspire meaningful ideas through AI, innovation, and storytelling. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx