Mine over Yours: How Authorship Biases Evaluation in Generative Information Retrieval
Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026) 2026
Generative Information RetrievalInteractive RetrievalAuthorship BiasIKEA EffectInformation TrustQuality JudgmentHuman-AI Interaction
Abstract
Generative information retrieval (GenIR) enables users to obtain information through iterative LLM interaction rather than merely retrieving existing documents. We examine whether this active involvement biases users’ evaluation of AI-crafted information—specifically, whether users judge information they obtained through their own interaction more favorably than equivalent information retrieved from others. In a mixed-methods experiment (N=28, 2x2 within-subjects), participants used an AI system to iteratively retrieve and craft informational content, then evaluated their results against equivalent AI-curated information retrieved by others. Results reveal a selective authorship bias: participants significantly overrated self-obtained information on quality, but maintained uniform trust ratings across conditions, reflecting hallucination awareness that nonetheless failed to correct quality-driven selection behavior. Higher effort further amplified this selection bias despite the presence of information conflicts. Since the iterative interaction that triggers this bias is inherent to GenIR, these findings point to a structural challenge requiring system-level safeguards for critical information evaluation.