0 papers in the Decision Making In Ai Systems research area.
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.
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, 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
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
Authors: Z Li, M Yin
Published in: Advances in Neural Information Processing ..., 2024 - proceedings.neurips.cc
Institution: Purdue University
Research Area: Human Behavior Modeling, Explainable AI (XAI), Decision Making in AI systems.
DOI: https://doi.org/10.52202/079017-0163
Citations: 7
Related Disciplines: Human-Computer Interaction, Artificial Intelligence, Behavioral Science
Related Institutions: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University, Massachusetts Institute of Technology, Yale University, Washington University in St. Louis, Purdue University
Researchers: S de Jong, V Paananen, B Tag, LS Treiman, CJ Ho, W Kool, Z Li, M Yin
Publication Years: 2025, 2024
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