Fairness In Ai: Research Area — Prolific Citations Library

Discover 5 peer-reviewed studies in Fairness In Ai (2023–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 5 peer-reviewed papers in the research area of Fairness In Ai 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
First-person fairness in chatbots

Authors: T Eloundou, A Beutel, DG Robinson

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: OpenAI, Google DeepMind, Google, University of Oxford

Research Area: Fairness in LLM, AI Bias, AI Ethics

Discipline: Artificial Intelligence, Social Science

The paper introduces a counterfactual approach to evaluate 'first-person fairness' in chatbots, demonstrating that reinforcement learning can mitigate biases based on demographics across extensive chatbot interactions.

Methods: The study uses a Language Model as a Research Assistant (LMRA) to quantitatively and qualitatively assess biases based on demographics across millions of chatbot interactions, covering 66 tasks in 9 domains and involving two genders and four races. Bias evaluations are corroborated by independent...

Key Findings: Demographic biases in chatbot responses, including harmful stereotypes and response differences by gender and race, across diverse tasks and domains.

DOI: https://doi.org/10.48550/arXiv.2410.19803

Citations: 33

Sample Size: 6000000
Whose preferences? differences in fairness preferences and their impact on the fairness of AI utilizing human feedback

Authors: M Lerner, F Dorner, E Ash, N Goel

Year: 2024

Published in: ... of the 62nd Annual Meeting of ..., 2024 - aclanthology.org

Institution: ETH Zürich, University of Oxford

Research Area: Fairness in AI, Content Moderation, Human-AI Alignment

Discipline: Computational Social Science

Citations: 5
Humans forgo reward to instill fairness into AI

Authors: LS Treiman, CJ Ho, W Kool

Year: 2023

Published in: Proceedings of the AAAI Conference on ..., 2023 - ojs.aaai.org

Institution: Massachusetts Institute of Technology, Yale University

Research Area: AI Ethics, Human-AI Interaction, Fairness in Machine Learning Training

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

DOI: https://doi.org/10.1609/hcomp.v11i1.27556

Citations: 6