G Lima: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by G Lima in Algorithmic Fairness and Human Perception (2025). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by G Lima in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

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