Human feedback can decrease AI trust in objective tasks, study finds

A new study published on arXiv explores how human-in-the-loop (HITL) feedback impacts user trust and perceived accuracy in AI systems. The research indicates that when tasks have objectively correct answers, user feedback can paradoxically decrease trust and accuracy perception over time. However, in subjective task domains, this negative bias is not observed, and users' trust in the system remains more stable. AI

IMPACT Understanding how user feedback influences trust is crucial for designing effective and reliable AI systems, especially in applications requiring objective accuracy.

RANK_REASON Research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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