Discover 3 peer-reviewed studies in Social Science In Ai (2024–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 3 peer-reviewed papers in the research area of Social Science In Ai in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: M Steyvers, H Tejeda, A Kumar, C Belem
Year: 2025
Published in: Nature Machine ..., 2025 - nature.com
Institution: University of California Irvine
Research Area: Computational Linguistics, Computational Social Science, AI Ethics, Trust in AI
Discipline: Computational Social Science
LLMs often lead to user overestimation of response accuracy, especially with longer explanations; adjusting explanation styles to align with model confidence improves calibration and discrimination gaps, enhancing trust in AI-assisted decision making.
Methods: Conducted experiments using multiple-choice and short-answer questions to study user confidence versus model-stated confidence; varied explanation length and alignment with model internal confidence.
Key Findings: Calibration gap (human vs. model confidence), discrimination gap (ability to distinguish correct vs. incorrect answers), and effects of explanation style and length on user trust.
Citations: 100
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Authors: D Guilbeault, S Delecourt, T Hull, BS Desikan, M Chu
Year: 2024
Published in: Nature, 2024 - nature.com
Institution: University of California Berkeley, Institute For Public Policy Research, Columbia University, University of Southern California Los Angeles
Research Area: Gender Bias, Computational Social Science, Online Media, AI Bias
Discipline: Computational Social Science
Online images significantly amplify gender bias compared to text, with biases in visual content impacting societal beliefs about gender roles.
Methods: Analyzed 3,495 social categories using over one million images from platforms like Google, Wikipedia, and IMDb, compared visual content to billions of words from the same platforms, and conducted a preregistered national experiment to assess the psychological impact on participants' beliefs.
Key Findings: The prevalence and psychological impact of gender bias in online images compared to text, including gender associations and representation disparities.
DOI: https://doi.org/10.1038/s41586-024-07068-x
Citations: 72
Sample Size: 3495
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Authors: Jacob Beck, Stephanie Eckman, Bolei Ma, Rob Chew, Frauke Kreuter
Year: 2024
Published in: ACL Anthology
Institution: University of Maryland
Research Area: Annotation Sensitivity, Order Effects, Natural Language Processing, Social Science in AI
Discipline: Natural Language Processing (NLP), Computational Social Science