Discover 11 peer-reviewed studies in Human Ai Decision Making (2022–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 11 peer-reviewed papers in the research area of Human Ai Decision Making in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: N Grgić-Hlača, G Lima, A Weller
Year: 2025
Published in: Proceedings of the 2nd ..., 2022 - dl.acm.org
Institution: Max Planck Institute, École Polytechnique Fédérale de Lausanne, University of Cambridge, The Alan Turing Institute
Research Area: Algorithmic Fairness, Human Perception, Diversity in AI Decision-Making
Discipline: Social Science, Artificial Intelligence
This study examines how sociodemographic factors and personal experience influence perceptions of fairness in algorithmic decision-making, particularly in bail decisions, highlighting the importance of diverse perspectives in regulatory oversight.
Methods: Explored perceptions of procedural fairness using surveys to assess the influence of demographics and personal experiences.
Key Findings: Impact of demographics (age, education, gender, race, political views) and personal experience on perceptions of fairness of algorithmic feature use in bail decisions.
DOI: 10.1145/3551624.3555306
Citations: 62
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Authors: JY Bo, S Wan, A Anderson
Year: 2025
Published in: Proceedings of the 2025 CHI Conference ..., 2025 - dl.acm.org
Institution: University of Toronto
Research Area: Appropriate reliance on LLM, Human-Computer Interaction, AI-assisted decision making.
Discipline: Human-Computer Interaction
This paper explores the latest advancements and key trends in the field of Human-Computer Interaction (HCI), focusing on novel interfaces and user experience paradigms.
Citations: 25
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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, 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: M Zhuang, E Deschrijver, R Ramsey, O Turel
Year: 2025
Published in: Scientific Reports, 2025 - nature.com
Institution: Monash University, The University of Melbourne, KU Leuven, California State University Fullerton
Research Area: Human-AI Interaction, Social Bias, Decision Making
Discipline: Social Science, Human-AI Interaction
The study found that humans exhibit similar discriminatory behavior toward both AI and human agents, with resource allocation being influenced more by decision alignment than the recipient's identity.
Methods: A preregistered experiment was conducted where participants distributed resources between themselves and either human or AI agents based on dot estimation decisions.
Key Findings: Discriminatory behavior and resource allocation preferences toward AI and human agents as influenced by decision congruency.
DOI: https://doi.org/10.1038/s41598-025-94631-9
Sample Size: 500
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Authors: K Vodrahalli, R Daneshjou, T Gerstenberg
Year: 2024
Published in: Proceedings of the 2022 ..., 2022 - dl.acm.org
Institution: Stanford University, Massachusetts Institute of Technology
Research Area: Trust in AI, Human-AI Interaction, Decision Making
Discipline: Human-AI Interaction, Decision Science
Humans' trust in AI advice is influenced by their beliefs about AI performance, and once they accept AI advice, they treat it similarly to advice from human peers.
Methods: Crowdworkers participated in several experimental settings to evaluate how participants respond to AI versus human suggestions and characterize user behavior with a proposed activation-integration model.
Key Findings: The influence of AI advice compared to human advice on decision-making and the behavioral factors affecting the use of such advice.
DOI: 10.1145/3514094.3534150
Citations: 99
Sample Size: 1100
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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 (XAI), Decision Making in AI systems.
Discipline: Artificial Intelligence, Behavioral Science
DOI: https://doi.org/10.52202/079017-0163
Citations: 7
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Authors: P Schoenegger, P Park, E Karger, P Tetlock
Year: 2024
Published in: ArXiv
Institution: Federal Reserve Bank of Chicago, London School of Economics and Political Science, Massachusetts Institute of Technology, University of Pennsylvania
Research Area: LLM Assistants, Human Forecasting, Predictive Modeling, AI-Augmented Decision Making, Large Language Models
Discipline: Artificial Intelligence, Behavioral Science
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Authors: P Hemmer, M Schemmer, N Kühl, M Vössing, G Satzger
Year: 2024
Published in: ArXiv
Institution: Karlsruhe Institute of Technology
Research Area: Human-AI Collaboration, Explainable AI (XAI), Complementarity in Decision Making
Discipline: Human-Computer Interaction
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Authors: JC Cresswell, Y Sui, B Kumar, N Vouitsis
Year: 2024
Published in: ArXiv
Institution: Layer6
Research Area: Human-AI Decision Making, Conformal Prediction, Trust in AI
Discipline: Artificial Intelligence
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Authors: H Vasconcelos, M Jörke
Year: 2023
Published in: Proceedings of the ..., 2023 - dl.acm.org
Institution: Stanford University, University of Washington
Research Area: Human-AI Interaction, Explainable AI (XAI), Decision Making
Discipline: Human-Computer Interaction, Artificial Intelligence
DOI: https://doi.org/10.1145/3579605
Citations: 405
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Authors: K Vodrahalli, T Gerstenberg
Year: 2022
Published in: Advances in Neural ..., 2022 - proceedings.neurips.cc
Institution: Columbia University, Princeton University, Intel, Stanford University, Massachusetts Institute of Technology
Research Area: Human-AI Collaboration, Human Behavior Modeling, Decision Making
Discipline: Artificial Intelligence
Citations: 70
