Fairness Perception: Research Area — Prolific Citations Library

Discover 5 peer-reviewed studies in Fairness Perception (2022–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 5 peer-reviewed papers in the research area of Fairness Perception in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (5 of 5)

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
Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice

Authors: G Lima, N Grgić-Hlača, M Langer, Y Zou

Year: 2025

Published in: Proceedings of the 2025 CHI ..., 2025 - dl.acm.org

Institution: University of Maryland, Max Planck Institute, Stanford University, Cornell University

Research Area: Algorithmic Fairness, Systemic Injustice, Social Perception of AI, Algorithmic Discrimination

Discipline: Computational Social Science

The study examines how contextualizing algorithms within systemic injustice impacts perceptions of algorithmic discrimination, finding disparate effects based on participant group identity and revealing unintended consequences of such contextualization.

Methods: 2x3 between-participants experiment using the hiring context as a case-study; examined the influence of systemic injustice information and algorithms' bias perpetuation on lay perceptions.

Key Findings: Impact of systemic injustice framing and explanation of algorithmic bias perpetuation on participants' views of algorithmic fairness and discrimination.

DOI: 10.1145/3706598.3713536

Citations: 2

Sample Size: 716
Applicants' fairness perceptions of algorithm-driven hiring procedures

Authors: M Lavanchy, P Reichert, J Narayanan

Year: 2023

Published in: Journal of Business ..., 2023 - Springer

Institution: Hong Kong Polytechnic University, International Institute for Management Development

Research Area: Applicants' Fairness Perceptions, Algorithm-Driven Hiring, Business Ethics

Discipline: Business Ethics, Human-Computer Interaction (HCI)

DOI: https://doi.org/10.1007/s10551-022-05320-w

Citations: 107
For me or against me? Reactions to AI (vs. human) decisions that are favorable or unfavorable to the self and the role of fairness perception

Authors: J Choi, MM Chao

Year: 2022

Published in: Personality and Social Psychology ..., 2024 - journals.sagepub.com

Research Area: Personality and Social Psychology, Human-AI Interaction, Fairness Perception

Discipline: Social Science, Artificial Intelligence, Psychology

DOI: https://doi.org/10.1177/01461672241288338

Citations: 5
Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States

Authors: M Mourali, D Novakowski, R Pogacar, N Brigden

Year: 2022

Published in: PloS one, 2025 - journals.plos.org

Institution: Haskayne School of Business, University of Calgary

Research Area: Algorithmic Fairness, Public Perception, AI Ethics

Discipline: Artificial Intelligence, AI Ethics, Social Psychology

Citations: 2