ethics-governanceQ7to verify
Glickman & Sharot 2024/25 — human-AI feedback loops amplify human bias (Nature Human Behaviour)
When humans interact iteratively with an AI system that has been trained on their own (mildly) biased judgments, the AI's outputs amplify the initial bias and subsequent human judgments become more biased than the baseline — establishing a measurable bidirectional bias-amplification loop across perceptual, emotional, and social judgement tasks.
Change in human judgment bias after iterated exposure to AI predictions trained on the same humans' baseline (biased) judgmentsBias amplification observed across perceptual, emotional, and social judgement tasks; quantitative effect sizes (Cohen's d, % shift) not extracted to verification.
- Sample
- Multiple experiments across perceptual, emotional, and social judgement domains; total N and per-experiment N not extracted to verification.
- Methodology
- Controlled feedback-loop experiments alternating human judgments with AI-provided judgments where the AI had been trained on the participants' own baseline (biased) responses; measured drift in human bias across rounds.
What this means
- Direct empirical demonstration of a niche-construction-style feedback loop in human-AI judgement: small initial bias → AI training → AI amplification → human re-exposure → increased bias.
- Suggests bias-mitigation evaluations that test AI in isolation (one-shot, no feedback) will systematically underestimate bias risk in deployed systems with recurring human-AI exchange.
- Provides the strongest single empirical anchor for the encyclopedia's argument that AI is a niche-constructing technology rather than a neutral tool — the loop is not theoretical, it is measured.
Source
How human-AI feedback loops alter human perceptual, emotional and social judgements
Nature Human Behaviour · Moshe Glickman & Tali Sharot · 2024 · peer-reviewed
Context
- What came before
- Algorithmic-bias literature focused largely on static evaluation: 'does this trained model produce biased outputs given fixed inputs?' Feedback dynamics — bias-as-loop, not bias-as-snapshot — were under-instrumented.
- What comes next
- Verify exact effect-size numbers from the published paper. Connect to the long-context-emergence + calibration-of-personalization AHI reviews (PA-001, PA-002) as related feedback-mechanism cases. Penwright measurement framework's bias-loop failure mode pairs with this finding.
- Where this lands
- Encyclopedia Part I §1.3 (methodology gap), Part V (research frontier — non-negotiable failure modes), Part VI (governance — paternalism vs autonomy).