Self-Blinding and Counterfactual Self-Simulation Mitigate Biases and Sycophancy in LLMs

Fair decisions require ignoring irrelevant, potentially biasing, information: a decision-maker must approximate what they would have decided had they not known, say, a job candidate's gender or race. This counterfactual self-simulation is notoriously hard for humans, producing biased judgments even by well-meaning actors. We show that large language models (LLMs) share this limitation, in offsetting gender and race biases and in overcoming sycophancy: prompting models to ignore or pretend not to know biasing information fails – and occasionally backfires. Unlike humans, however, LLMs can be given a ground-truth model of their own counterfactual cognition: their own API. Enabling models to query blinded copies of themselves yields fairer decisions and an auditable record of every departure from the blinded verdict.