Federated Approaches to Data Challenges in Ethical AI

Federated Approaches to Data Challenges in Ethical AI

In my recent presentation, I explored the modern data-driven AI model, highlighting its ethical challenges, including privacy violations and biases from unconsented data collection. I discussed current mitigation strategies and their limitations, emphasizing the need for a better solution.

Federated Learning emerged as a promising alternative, leveraging decentralized data from multiple devices while preserving user privacy. Drawing parallels to community-owned microgrids in energy, I proposed this approach for democratizing AI and ensuring ethical data use. This model allows users to retain control over their data, fostering transparency and fairness in AI development.