Panel: Is Decentralization The Best Way to Achieve Transparency and Verifiability in AI?

Decentralized AI can enhance transparency and accountability by distributing data, computation, validation, optimization, and execution across multiple nodes, thus preventing a concentration of power that could threaten individual freedoms.
It can also foster innovation by allowing third-party developers to verify the data and algorithms that an AI system uses.
Technologies such as blockchain and federated learning are at the forefront of this revolution. Blockchain's transparency, immutability, and decentralization can enhance trust in data-driven decision-making.
Federated learning, on the other hand, allows for the training of machine learning models on decentralized data, thus preserving privacy and reducing the need for data centralization. In this panel session we will discuss the technical foundations and the pros and cons of decentralized AI.