Ai Bias: Research Area — Prolific Citations Library

Discover 19 peer-reviewed studies in Ai Bias (2022–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 19 peer-reviewed papers in the research area of Ai Bias in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (19 of 19)

The Artificial Intelligence Disclosure Penalty: Humans Persistently Devalue AI-Generated Creative Writing

Authors: M Raj, JM Berg, R Seamans

Year: 2026

Published in: Journal of Experimental Psychology …, 2026 - psycnet.apa.org

Institution: New York University, University of Michigan, Wharton

Research Area: Disclosure psychology, Biases in human–machine evaluation, AI Biases

Discipline: Experimental psychology

This paper sits at the intersection of experimental psychology, social cognition, and consumer judgment, examining how AI disclosure triggers persistent authenticity-based bias against creative work, revealing a robust form of algorithmic aversion in symbolic and expressive domains.

DOI: https://doi.org/10.1037/xge0001889
The placebo effect of artificial intelligence in Human-Computer Interaction (HCI)

Authors: T Kosch, R Welsch, L Chuang, A Schmidt

Year: 2025

Published in: ACM Transactions on ..., 2023 - dl.acm.org

Institution: Aalto University

Research Area: User Expectations, HCI Research Bias, Artificial Intelligence, AI Bias

Discipline: Human-Computer Interaction (HCI)

The belief in receiving adaptive AI support positively impacts user performance, demonstrating a placebo effect in Human-Computer Interaction.

Methods: Two experiments where participants completed word puzzles under conditions with or without supposed AI support; in reality, no AI assistance was provided.

Key Findings: Impact of perceived AI support on user expectations and task performance.

DOI: https://doi.org/10.1145/3529225

Citations: 149

Sample Size: 469
How do people react to political bias in generative Artificial Intelligence?

Authors: U Messer

Year: 2025

Published in: Computers in Human Behavior: Artificial Humans, 2025 - Elsevier

Institution: Universität der Bundeswehr München

Research Area: Political Bias in Generative AI, Human-AI Interaction, Affective Computing, AI Bias

Discipline: Computer Science, Human-AI Interaction

People's acceptance and reliance on Generative AI (GAI) increase when they perceive alignment between their political orientation and the bias of GAI-generated content, leading to expanded trust in sensitive applications.

Methods: Three experiments analyzing behavioral reactions to politically biased content generated by GAI, including the impact of perceived alignment on acceptance and trust.

Key Findings: Participants' acceptance, reliance, and trust in GAI based on perceived alignment between political bias of GAI-generated content and their own political beliefs.

DOI: https://doi.org/10.1016/j.chbah.2024.100108

Citations: 24

Sample Size: 513
Age and gender distortion in online media and large language models

Authors: D Guilbeault, S Delecourt, BS Desikan

Year: 2025

Published in: Nature, 2025 - nature.com

Institution: Stanford University, University of California Berkeley, University of Oxford

Research Area: AI Bias, Media Representation, Social Science

Discipline: Computational Social Science, Artificial Intelligence

The study highlights age-related gender bias in online media and language models, showing women are portrayed as younger than men, especially in high-status occupations, and explores how algorithms amplify these biases.

Methods: Analysis of 1.4 million images and videos from online sources and nine language models, followed by a pre-registered experiment involving participants to evaluate biases in internet content and algorithms.

Key Findings: Age and gender bias in occupational depiction across online platforms and language models, as well as its influence on beliefs and hiring preferences.

Citations: 4

Sample Size: 459
To Mask or to Mirror: Human-AI Alignment in Collective Reasoning

Authors: C Qian, AT Parisi, C Bouleau, V Tsai

Year: 2025

Published in: Proceedings of the ..., 2025 - aclanthology.org

Institution: Google, Google DeepMind

Research Area: Human-AI Alignment, Collective Reasoning, Social Biases, LLM Simulation of Human Behavior, AI Bias

Discipline: Natural Language Processing, Artificial Intelligence, Computational Social Science

This study examines human-AI alignment in collective reasoning using an empirical framework, demonstrating how LLMs either mirror or mask human biases depending on context, cues, and model-specific inductive biases.

Methods: The study uses the Lost at Sea social psychology task in a large-scale online experiment, simulating LLM groups conditioned on human decision-making data across varying conditions of visible or pseudonymous demographics.

Key Findings: Alignment of LLM behavior with human social reasoning, focusing on collective decision-making and biases in group interactions.

Citations: 1

Sample Size: 748
Biased By Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers

Authors: Liudmila Zavolokina, Kilian Sprenkamp, Zoya Katashinskaya, Daniel Gordon Jones

Year: 2025

Published in: ArXiv

Institution: University of Zurich

Research Area: AI Ethics, AI Bias, News Literacy, Critical Thinking, Computational Social Science

Discipline: Computational Social Science

The study explores leveraging inherent biases in AI to enhance critical thinking in news consumption, proposing strategies such as bias awareness, personalization, and gradual introduction of diverse perspectives.

Methods: Qualitative user study investigating user responses to personalized AI-driven propaganda detection tools.

Key Findings: The effectiveness of AI bias-based strategies in improving critical thinking and news readers’ engagement with diverse perspectives.
Comparing discriminatory behavior against AI and humans

Authors: M Zhuang, E Deschrijver, R Ramsey, O Turel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: Monash University, The University of Melbourne, KU Leuven, California State University Fullerton

Research Area: Human-AI Interaction, Social Bias, Decision-Making

Discipline: Social Science, Human-AI Interaction

The study found that humans exhibit similar discriminatory behavior toward both AI and human agents, with resource allocation being influenced more by decision alignment than the recipient's identity.

Methods: A preregistered experiment was conducted where participants distributed resources between themselves and either human or AI agents based on dot estimation decisions.

Key Findings: Discriminatory behavior and resource allocation preferences toward AI and human agents as influenced by decision congruency.

DOI: https://doi.org/10.1038/s41598-025-94631-9

Sample Size: 500
Multimodal large language models can make context-sensitive hate speech evaluations aligned with human judgement

Authors: T Davidson

Year: 2025

Published in: Nature Human Behaviour, 2025 - nature.com

Institution: University of Oxford, Davidson College

Research Area: Hate Speech Evaluation, Multimodal LLMs, Social Bias, Computational Law, AI Bias, AI Evaluation

Discipline: Artificial Intelligence

The study demonstrates that larger multimodal large language models (MLLMs) can align closely with human judgement in context-sensitive hate speech evaluations, though they still exhibit biases and limitations.

Methods: Conjoint experiments where simulated social media posts varying in attributes like slur usage and user demographics were evaluated by MLLMs and compared to human judgements.

Key Findings: The capacity of MLLMs to evaluate hate speech in a context-sensitive manner and their alignment with human judgement, while assessing biases and responsiveness to contextual cues.

Sample Size: 1854
Online images amplify gender bias

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
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
Large language models amplify human biases in moral decision-making

Authors: V Cheung, M Maier, F Lieder

Year: 2024

Published in: Psyarxiv preprint, 2024 - files.osf.io

Institution: University College LondonA

Research Area: AI Ethics, Moral Decision-Making, Cognitive Biases in LLMs, AI Bias

Discipline: Artificial Intelligence, Ethics

Citations: 11
Benchmarking Distributional Alignment of Large Language Models

Authors: N Meister

Year: 2024

Published in: ArXiv

Institution: Stanford University

Research Area: Distributional Alignment of LLMs, LLM Benchmarking, AI Robustness, AI Fairness, AI Bias

Discipline: Artificial Intelligence
Confirmation bias in AI-assisted decision-making: AI triage recommendations congruent with expert judgments increase psychologist trust and recommendation acceptance

Authors: A Bashkirova, D Krpan

Year: 2024

Published in: Science Direct

Institution: London School of Economics and Political Science

Research Area: AI-assisted Decision Making, Confirmation Bias, Professional Trust, Psychology, AI Bias

Discipline: Behavioral Science, Psychology
Does Differential Privacy Impact Bias in Pretrained NLP Models?

Authors: Md. Khairul Islam1, Andrew Wang1, Tianhao Wang1, Yangfeng Ji1, Judy Fox 1, Jieyu Zhao2

Year: 2024

Published in: ArXiv

Institution: University of Virginia

Research Area: Differential Privacy, Bias Mitigation, LLM, Natural Language Processing (NLP), AI Bias

Discipline: Artificial Intelligence, Natural Language Processing
Mitigating Bias in Reinforcement Learning from Human Feedback for Large Language Models

Authors: C Ravulu, R Sarabu, M Suryadevara

Year: 2024

Published in: ... Conference on AI x ..., 2024 - ieeexplore.ieee.org

Institution: International Institute of Information Technology, University of California Santa Cruz, University of South Carolina Aiken

Research Area: Reinforcement Learning from Human Feedback (RLHF), Bias Mitigation, LLM, AI Bias

Discipline: Artificial Intelligence

DOI: https://ieeexplore.ieee.org/abstract/document/10990073/
Multilingual large language models leak human stereotypes across language boundaries

Authors: Yang Trista Cao, Anna Sotnikova, Jieyu Zhao, Linda X. Zou, Rachel Rudinger, Hal Daumé III

Year: 2024

Published in: ArXiv

Institution: Microsoft Research, University of Maryland

Research Area: Multilingual Bias, Social Science, LLM, AI Bias

Discipline: Artificial Intelligence, Social Science, Large Language Models
Super-intelligence or Superstition? Exploring Psychological Factors Underlying Unwarranted Belief in AI Predictions

Authors: Eunhae Lee, Pat Pataranutaporn, Judith Amores, Pattie Maes

Year: 2024

Published in: ArXiv

Institution: Massachusetts Institute of Technology, Microsoft Research, MIT Media Lab

Research Area: Human-AI Interaction, Cognitive Biases, Psychological Factors in AI Adoption, Trust in AI, AI Credibility

Discipline: Psychology, Artificial Intelligence
The role of affective and cognitive involvement in the mitigating effects of AI source cues on hostile media bias

Authors: Matthew J.A. Craig a, Mina Choi b

Year: 2023

Published in: Science Direct

Institution: Kent State University, Sejong University

Research Area: Media Studies, Social Psychology, Cognitive Bias, AI and Communication, AI Bias

Discipline: Social Science, Media Studies, Human-AI Interaction
Faces merely labelled as artificial are trusted less

Authors: B Liefooghe, M Oliveira, LM Leisten, E Hoogers

Year: 2022

Published in: 2022 - research-portal.uu.nl

Institution: Utrecht University

Research Area: Trust in AI, Synthetic Media, Perceived Bias, AI Bias

Discipline: Artificial Intelligence, Psychology

DOI: https://doi.org/10.31234/osf.io/te2ju

Citations: 5