Today, I explored the audio version of a chapter from the AI Safety Atlas as part of my AI safety studies. Below is the resource I reviewed.
Resource: AI Safety Atlas (Chapter 9: Interpretability Audio)
- Source: Chapter 9: Interpretability , AI Safety Atlas by Markov Grey and Charbel-Raphaël Segerie et al., French Center for AI Safety (CeSIA), 2025.
- Summary: The audio version of this chapter explores the field of interpretability, which seeks to make the decision-making processes of complex AI models understandable to humans. It discusses the inherent risks of ‘black box’ systems and covers various techniques for analyzing and visualizing model internals. This transparency is crucial for debugging, verifying alignment, and building trust in advanced AI systems.