Decision Making: Research Area — Prolific Citations Library

Discover 25 peer-reviewed studies in Decision Making (2023–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 25 peer-reviewed papers in the research area of Decision Making in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 25)

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
Capturing the complexity of human strategic decision-making with machine learning

Authors: JQ Zhu, JC Peterson, B Enke, TL Griffiths

Year: 2025

Published in: Nature Human Behaviour, 2025 - nature.com

Institution: Princeton University, Boston University, Harvard University

Research Area: Strategic decision-making, Machine learning, Computational Cognitive Science

Discipline: Artificial Intelligence

This study used deep neural networks to analyze human strategic decision-making, predicting choices more accurately than existing theories and uncovering the context-dependent nature of reasoning and decision-making in complex games.

Methods: Deep neural networks trained on data from procedurally generated matrix games with over 2,400 variations; models were modified for interpretability.

Key Findings: Human choices and reasoning in initial play of two-player matrix games, focusing on strategic decision-making and response to game complexity.

DOI: https://doi.org/10.1038/s41562-025-02230-5

Citations: 16

Sample Size: 90000
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
Imperfections of XAI: phenomena influencing AI-assisted decision-making

Authors: P Spitzer, K Morrison, V Turri, M Feng, A Perer

Year: 2025

Published in: ACM Transactions on ..., 2025 - dl.acm.org

Institution: Carnegie Mellon University, Karlsruhe Institute of Technology, University of Bayreuth

Research Area: Explainable AI (XAI), AI-Assisted Decision-Making, Human-AI Collaboration

Discipline: Artificial Intelligence

The study highlights how imperfect explainable AI (XAI), along with human cognitive styles, affects reliance on AI and the performance of human–AI teams, providing design guidelines for better collaboration systems.

Methods: The researchers conducted a study with 136 participants, analyzing the effects of explanation imperfections and cognitive styles on AI-assisted decision-making and human–AI collaboration.

Key Findings: The impact of incorrect explanations and explanation modalities on human reliance, decision-making, and human–AI team performance, as well as the role of cognitive styles.

Citations: 2

Sample Size: 136
Human-AI Collaboration in High-Stakes Decision-Making: Work in Progress

Authors: J Zhou, R Aloufi, N van Zalk

Year: 2025

Published in: 38th International BCS Human ..., 2025 - scienceopen.com

Institution: The affiliated institutions for the authors are not available in the provided context or search results.

Research Area: High-Stakes Decision-Making, Explainable AI, User Trust, Human-Centered AI, Interaction Design

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

This study explores how human collaboration and communication dynamics vary when interacting with an AI chatbot versus a human partner in a high-stakes decision-making task.

Methods: One-way between-subjects design using the NASA Moon Survival Task to compare behaviors, linguistic coordination, and perceptions in interactions with AI or human partners.

Key Findings: Collaboration processes, communicative dynamics, outcomes, retrospective interaction experience, partner perception, and linguistic coordination, with user profiling for AI benefit variations.

DOI: doi.org/10.14236/ewic/BCSHCI2025.52

Citations: 1
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
Fairness Perceptions in Regression-based Predictive Models

Authors: Mukund Telukunta, Venkata Sriram Siddhardh Nadendla, Morgan Stuart, Casey Canfield

Year: 2025

Published in: ArXiv

Institution: Missouri University of Science and Technology, United Network for Organ Sharing

Research Area: Algorithmic Fairness, Healthcare AI, Decision-Making

Discipline: Artificial Intelligence

The study investigates fairness in regression-based predictive models for kidney transplantation, introducing three group fairness notions and identifying social preferences for fairness criteria, revealing biases against age groups but fairness towards gender and race groups.

Methods: Three novel fairness notions (independence, separation, sufficiency) were introduced alongside crowd feedback analysis through a Mixed-Logit discrete choice model.

Key Findings: Fairness in regression-based predictive analytics regarding group fairness criteria across social dimensions such as age, gender, and race.

Sample Size: 85
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
Understanding the Role of Explanation Modality in AI-assisted Decision-making

Authors: V Robbemond, O Inel, U Gadiraju

Year: 2024

Published in: ... of the 30th ACM Conference on ..., 2022 - dl.acm.org

Institution: V Robbemond: Delft University of Technology, O Inel: Delft University of Technology, U Gadiraju: Delft University of Technology

Research Area: Explanation Modality in AI-assisted Decision Making

Discipline: Human-Computer Interaction (HCI)

The study explores the role of explanation modalities in AI-assisted credibility assessment tasks, finding that combined modalities (text and/or audio with graphics) enhance accuracy, trust, and usability compared to single-modality approaches.

Methods: A between-subjects experiment was conducted with six explanation modalities to evaluate their influence on user performance, trust, and usability in credibility assessments.

Key Findings: The effects of different explanation modalities on decision accuracy, system trust, and usability in an AI-assisted credibility assessment system.

DOI: 10.1145/3503252.3531311

Citations: 47

Sample Size: 375
The consequences of AI training on human decision-making

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
Large language models amplify human biases in moral decision-making

Authors: V Cheung, M Maier, F Lieder

Year: 2024

Published in: Psyarxiv preprint, 2024 - files.osf.io

Institution: University College LondonA

Research Area: AI Ethics, Moral Decision-Making, Cognitive Biases in LLMs, AI Bias

Discipline: Artificial Intelligence, Ethics

Citations: 11
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
Clinicians diagnosing virtual patients with the classification algorithm for chronic pain in the ICD-11 (CAL-CP) achieve better diagnoses and prefer the algorithm to standard tools: An experimental validation study

Authors: G Hay, Beatrice Korwisi, Norman Lahme-Hütig, Winfried Rief, Antonia Barke

Year: 2024

Published in: Wiley

Institution: Marburg University, Münster School of Business, University of Duisburg-Essen

Research Area: ICD-11 Chronic Pain Classification, Clinical Diagnosis, Algorithmic Decision-Making

Discipline: Health, Medicine, Artificial Intelligence
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)
Confirmation bias in AI-assisted decision-making: AI triage recommendations congruent with expert judgments increase psychologist trust and recommendation acceptance

Authors: A Bashkirova, D Krpan

Year: 2024

Published in: Science Direct

Institution: London School of Economics and Political Science

Research Area: AI-assisted Decision Making, Confirmation Bias, Professional Trust, Psychology, AI Bias

Discipline: Behavioral Science, Psychology
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
Generative AI Triggers Welfare-Reducing Decisions in Humans

Authors: Fabian Dvorak, Regina Stumpf, Sebastian Fehrler, Urs Fischbacher

Year: 2024

Published in: ArXiv

Institution: University of Konstanz

Research Area: Generative AI and Human Decision-Making, Behavioral Economics

Discipline: Artificial Intelligence, Behavioral Economics
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