National Institutes Of Health Papers

Browse 1 peer-reviewed paper from National Institutes Of Health spanning Human-Computer Interaction (2025). Research powered by Prolific's high-quality participant data.

This page lists 1 peer-reviewed paper from researchers at National Institutes Of Health in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

Bias in the loop: How humans evaluate AI-generated suggestions

Authors: J Beck, S Eckman, C Kern, F Kreuter

Year: 2025

Published in: arXiv preprint arXiv:2509.08514, 2025 - arxiv.org

Institution: National Institutes of Health, National Center for Biotechnology Information

Research Area: Human-Computer Interaction

Discipline: Human-Computer Interaction

Human attitudes toward AI strongly influence performance in collaborative tasks, with skeptics showing better error detection and accuracy, while automation favorability increases overreliance on AI suggestions.

Methods: Randomized experiment with a controlled annotation task manipulating AI suggestion quality, task burden, and performance-based financial incentives; collected demographic, attitudinal, and behavioral data.

Key Findings: Impact of AI suggestion quality, task burden, and financial incentives on participant performance metrics (accuracy, correction activity, overcorrection, undercorrection); influence of demographic and psychological characteristics on performance.

Citations: 4

Sample Size: 2784