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