Social Perception Of Ai: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Social Perception Of Ai (2024–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Social Perception Of Ai in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

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
AI can help people feel heard, but an AI label diminishes this impact

Authors: Y Yin, N Jia, CJ Wakslak

Year: 2024

Published in: Proceedings of the National Academy of ..., 2024 - pnas.org

Institution: University of Southern California Los Angeles

Research Area: Human-AI Interaction, Social Perception of AI, Media Effects

Discipline: Social Sciences

AI responses make people feel more heard and are better at emotional support compared to humans, but labeling responses as AI diminishes this effect.

Methods: Experiment and follow-up study to assess recipient reactions to AI vs. human-generated responses and determine emotional support efficacy.

Key Findings: The degree to which recipients feel heard, emotion detection accuracy, and third-party ratings of emotional support quality.

DOI: https://doi.org/10.1073/pnas.2319112121

Citations: 201