Human Ai Decision Making: Research Area — Prolific Citations Library

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.

Papers (11 of 11)

Dimensions of diversity in human perceptions of algorithmic fairness

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
To rely or not to rely? evaluating interventions for appropriate reliance on large language models

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 (HCI), AI-assisted decision making.

Discipline: Human-Computer Interaction (HCI)

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
Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations

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
Comparing discriminatory behavior against AI and humans

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
Do humans trust advice more if it comes from ai? an analysis of human-ai interactions

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
Utilizing human behavior modeling to manipulate explanations in AI-assisted decision making: the good, the bad, and the scary

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
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy

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, LLM

Discipline: Artificial Intelligence, Behavioral Science
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence

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 (HCI)
Conformal Prediction Sets Improve Human Decision Making

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
Explanations can reduce overreliance on ai systems during decision-making

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 (HCI), Artificial Intelligence

DOI: https://doi.org/10.1145/3579605

Citations: 405
Uncalibrated models can improve human-ai collaboration

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