Discover 3 peer-reviewed studies in Decision Making In Ai Systems (2024–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 3 peer-reviewed papers in the research area of Decision Making In Ai Systems in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: S de Jong, V Paananen, B Tag
Year: 2025
Published in: Proceedings of the ACM on ..., 2025 - dl.acm.org
Institution: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University
Research Area: Cognitive Forcing, Human-AI Interaction, AI Explainability (XAI), Decision-Making in AI Systems.
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
Partial explanations encourage critical thinking and reduce user overreliance on incorrect AI suggestions, with performance varying based on individual need for cognition and task difficulty.
Methods: Two experiments were conducted: (1) participants identified shortest paths in weighted graphs, and (2) participants corrected spelling and grammar errors in text, with AI suggestions accompanied by no, partial, or full explanations.
Key Findings: Effectiveness of partial explanations in reducing overreliance on incorrect AI suggestions, and interaction of explanation type with task difficulty and user need for cognition.
DOI: https://doi.org/10.1145/3710946
Citations: 14
Sample Size: 474
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Authors: LS Treiman, CJ Ho, W Kool
Year: 2024
Published in: Proceedings of the National Academy of ..., 2024 - pnas.org
Institution: Massachusetts Institute of Technology, Yale University, Washington University in St. Louis
Research Area: AI Ethics, Behavioral Economics, Decision-Making in AI Systems
Discipline: Artificial Intelligence, Behavioral Science
People alter their behavior when they know their actions will train AI, leading to unintentional habits and biased training data for AI systems.
Methods: Five studies were conducted using the ultimatum game; participants were tasked with deciding on monetary splits proposed by either humans or AI, with some informed their decisions would train the AI.
Key Findings: Behavioral changes in participants when training AI, persistence of these changes over time, and implications for AI training bias.
DOI: https://doi.org/10.1073/pnas.2408731121
Citations: 13
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Authors: Z Li, M Yin
Year: 2024
Published in: Advances in Neural Information Processing ..., 2024 - proceedings.neurips.cc
Institution: Purdue University
Research Area: Human Behavior Modeling, Explainable AI, Decision Making in AI systems.
Discipline: Artificial Intelligence, Behavioral Science
DOI: https://doi.org/10.52202/079017-0163
Citations: 7