A Byzantine Fault Tolerance Approach towards AI Safety
It addresses the challenge of ensuring AI reliability in adversarial or faulty conditions for safety-critical applications.
The paper proposes a fault tolerance architecture for AI safety inspired by Byzantine Fault Tolerance, using consensus mechanisms to handle unreliable or malicious AI components.
Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed computing, we explore a fault tolerance architecture for AI safety. By drawing an analogy between unreliable, corrupt, misbehaving or malicious AI artifacts and Byzantine nodes in a distributed system, we propose an architecture that leverages consensus mechanisms to enhance AI safety and reliability.