When AI Advice Shapes the Evidence It Later Interprets: A Blind Spot in Social-Reasoning Evaluation

You scroll through Instagram and see a friend's photo of a dinner they hosted with some of your mutual friends — but you weren't there. A little hurt, you ask an AI assistant whether they think this was deliberate exclusion. The AI says it's possible but suggests you ask your friend directly, even helping you figure out how to bring it up. You follow that advice, but your friend responds defensively. You return to the conversation and report the result, and the AI might then become more confident: look, their defensive reaction confirms the original judgment — they really did exclude you on purpose.

LessWrong author rozenn (Ben-Gurion University postdoctoral email) argues that this scenario exposes an evaluation blind spot: the AI treats the friend's defensive reaction as independent evidence, yet that reaction itself may have been partly produced by the AI's own advice. A defensive response could come from someone who deliberately excluded you, or from someone who simply forgot to invite you but instinctively guards themselves when put on the spot. In both cases, defensiveness is plausible, so it does not necessarily provide additional support for deliberate exclusion as a hypothesis.

The author connects this logic to the social-psychology concept of behavioral confirmation. In Snyder and Swann's classic 1978 experiment, participants who were led to believe their opponent was hostile chose higher-intensity noise to direct at their opponent; the opponent then responded with louder noise in turn. After the game, those who had expected hostility judged their opponents as more aggressive — overlooking the possibility that their own behavior had shaped the response they later interpreted. Snyder and Swann described this as 'reality-testing has become reality-construction.'