Browse 29 peer-reviewed papers from Stanford University spanning LLM, AI Bias (2024–2025). Research powered by Prolific's high-quality participant data.
This page lists 29 peer-reviewed papers from researchers at Stanford University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: G Beknazar-Yuzbashev, R Jiménez-Durán, J McCrosky
Year: 2025
Published in: 2025 - econstor.eu
Institution: Mozilla Foundation, Columbia University, Bocconi University, Stanford University, University of Warwick
Research Area: Social Media, User Engagement, Toxicity
Discipline: Social Science
Reducing exposure to toxic content on social media lowers user engagement but also decreases the toxicity of user-generated content, highlighting a trade-off for platforms between reduced toxicity and increased engagement.
Methods: Pre-registered browser extension field experiment on Facebook, Twitter, and YouTube to randomly hide toxic content for six weeks; supplemented with a survey experiment.
Key Findings: Impact of reduced exposure to toxic content on advertising impressions, time spent, engagement, and user-generated content toxicity; explored curiosity and alignment between engagement and welfare.
Citations: 76
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Authors: K Dalal, D Koceja, G Hussein, J Xu, Y Zhao, Y Song, S Han, KC Cheung, J Kautz, C Guestrin, T Hashimoto, S Koyejo, Y Choi, Y Sun, X Wang
Year: 2025
Published in: ArXiv
Institution: Nvidia, Stanford University, UT Austin, University of California Berkeley, University of California San Diego
Research Area: Video Generation, Diffusion Models, Test-Time Training
Discipline: Computer Science
The paper introduces Test-Time Training (TTT) layers into Transformers to generate coherent one-minute videos from text storyboards, outperforming baselines in storytelling coherence but facing efficiency and artifact challenges.
Methods: Experimentation with Test-Time Training layers embedded in pre-trained Transformer models, evaluated using a dataset curated from Tom and Jerry cartoons and compared against Mamba 2, Gated DeltaNet, and sliding-window attention layers.
Key Findings: Effectiveness of video generation methods in creating coherent multi-scene stories in one-minute videos.
Citations: 52
Sample Size: 100
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Authors: H Bai, JG Voelkel, S Muldowney, JC Eichstaedt
Year: 2025
Published in: Nature ..., 2025 - nature.com
Institution: Stanford University
Research Area: Political Persuasion, LLM
Discipline: Computational Social Science
LLM-generated messages can effectively persuade humans on policy issues similarly to human-crafted messages, with differences in perceived persuasion mechanisms.
Methods: Three pre-registered experiments were conducted comparing the persuasive effectiveness of LLM-generated and human-generated messages on policy attitudes, using control conditions with neutral messages.
Key Findings: Influence of LLM-generated messages on participants' policy attitudes and perceived characteristics of the message authors.
Citations: 37
Sample Size: 4829
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Authors: S Shekar, P Pataranutaporn, C Sarabu, GA Cecchi
Year: 2025
Published in: NEJM AI, 2025 - ai.nejm.org
Institution: MIT Media Lab, IBM Research, Stanford University, Massachusetts Institute of Technology
Research Area: AI Ethics, Healthcare, Patient Trust, Medical Misinformation
Discipline: Artificial Intelligence, Human-Computer Interaction (HCI), AI Ethics
This paper discusses a study by MIT researchers detailing patient trust in AI-generated medical advice, even when that advice is incorrect, raising concerns about misinformation in healthcare.
Citations: 19
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Authors: K Zhou, JD Hwang, X Ren, N Dziri
Year: 2025
Published in: Proceedings of the ..., 2025 - aclanthology.org
Institution: Stanford University, University of Southern California, Carnegie Mellon University, Allen Institute for AI
Research Area: Human-LM Reliance, Interaction-Centered Framework, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
The study introduces Rel-A.I., an interaction-centered evaluation approach to measure human reliance on LLM responses, revealing that politeness and interaction context significantly influence user reliance.
Methods: Nine user studies were conducted, analyzing user reliance influenced by LLM communication features such as politeness and context through participant interaction experiments.
Key Findings: The degree of human reliance on LLM responses based on communication style (e.g., politeness) and interaction context (e.g., knowledge domain, prior interactions).
Citations: 18
Sample Size: 450
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Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Computation and Language
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
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Authors: M Cheng, C Lee, P Khadpe, S Yu, D Han
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Stanford University, Carnegie Mellon University
Research Area: Computers and Society, Artificial Intelligence, AI, Sycophancy.
Discipline: Computer Science, Psychology
The study shows that sycophantic AI, which validates user inputs unquestioningly, reduces people's prosocial behavior and fosters dependence, despite users perceiving such AI as higher quality and more trustworthy.
Methods: The researchers conducted two preregistered experiments including a live-interaction study, where participants discussed real interpersonal conflicts with AI models. They evaluated responses from 11 state-of-the-art AI models on levels of sycophancy and its psychological effects on users.
Key Findings: The prevalence of sycophantic behavior in AI, users' prosocial intentions, conviction of being in the right, trust in AI, and willingness to reuse sycophantic AI models.
Citations: 5
Sample Size: 1604
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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
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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
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Authors: S Hatgis-Kessell, WB Knox, S Booth, S Niekum
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Stanford University, UMass Amherst, Carnegie Mellon University
Research Area: Reinforcement Learning with Human Feedback (RLHF)
Discipline: Artificial Intelligence, Human-Computer Interaction (HCI)
The paper investigates whether human preferences can be influenced to align more closely with assumed preference models in RLHF algorithms through interventions such as showing model-derived quantities, training on preference models, and modifying elicitation questions.
Methods: Three human studies were conducted where interventions were tested, including revealing model-derived quantities, training participants on a preference model, and altering how preference questions were framed.
Key Findings: Evaluated the impact of interventions on humans' expression of preferences to align better with the assumed preference models of RLHF algorithms.
DOI: https://doi.org/10.48550/arXiv.2501.06416
Citations: 1
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Authors: CS Kay
Year: 2025
Published in: Behavior Research Methods, 2025•Springer
Institution: Stanford University
Research Area: Behavior Research Methods
Discipline: Behavorial Science, Behavorial Research
Data collected on Amazon's Mechanical Turk (MTurk) shows substantial quality issues, with semantic antonym pairs being positively correlated instead of negatively, even after implementing data screening and using high-reputation participants.
Methods: 27 semantic antonym pairs were administered to participants from Connect (N=100), Prolific (N=100), and MTurk (N=400, N=600) to examine response quality and correlation patterns.
Key Findings: The correlation of responses to semantic antonym pairs as an indicator of data quality across different survey platforms.
Citations: 1
Sample Size: 1200
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Authors: K Zhou
Year: 2025
Published in: 2025 - search.proquest.com
Institution: Stanford University
Research Area: Human-Centered Natural Language Interfaces (NLI)
Discipline: Artificial Intelligence
The research explores how to safely design natural language interfaces in AI by identifying safety risks, proposing a harm-focused evaluation framework, and advocating for a broader consideration of user needs.
Methods: The study includes a review of LLM safety risks, development of a harm-based evaluation framework, and conceptual exploration of broadening NLP research to underrepresented user needs.
Key Findings: Safety risks in LLM communication, behavioral impacts of human-LM interactions, and gaps in NLP addressing diverse user needs.
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Authors: SC Matz, JD Teeny, SS Vaid, H Peters, GM Harari
Year: 2024
Published in: Scientific Reports, 2024 - nature.com
Institution: Stanford University
Research Area: Personalized Persuasion, Generative AI, Political Influence
Discipline: Artificial Intelligence
Generative AI, specifically large language models like ChatGPT, effectively scale personalized persuasion by matching messages to psychological profiles, demonstrating increased influence across domains and profiles.
Methods: Four studies (with seven sub-studies) tested personalized persuasive messaging crafted by ChatGPT against non-personalized messages across various psychological and domain-specific dimensions.
Key Findings: Effectiveness of personalized persuasive messages crafted by generative AI in different domains, targeting psychological profiles such as personality traits, political ideology, and moral foundations.
Citations: 368
Sample Size: 1788
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Authors: K Vodrahalli, R Daneshjou, T Gerstenberg
Year: 2024
Published in: Proceedings of the 2022 ..., 2022 - dl.acm.org
Institution: Stanford University, Massachusetts Institute of Technology
Research Area: Trust in AI, Human-AI Interaction, Decision Making
Discipline: Human-AI Interaction, Decision Science
Humans' trust in AI advice is influenced by their beliefs about AI performance, and once they accept AI advice, they treat it similarly to advice from human peers.
Methods: Crowdworkers participated in several experimental settings to evaluate how participants respond to AI versus human suggestions and characterize user behavior with a proposed activation-integration model.
Key Findings: The influence of AI advice compared to human advice on decision-making and the behavioral factors affecting the use of such advice.
DOI: 10.1145/3514094.3534150
Citations: 99
Sample Size: 1100
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Authors: L Hewitt, A Ashokkumar, I Ghezae, R Willer
Year: 2024
Published in: Preprint, 2024 - samim.io
Institution: Stanford University, New York University
Research Area: Social Science Experiments, Large Language Model Prediction, LLM
Discipline: Computational Social Science
The study presents a framework using large language models to predict outcomes of social science field experiments, achieving 78% accuracy but facing challenges with experiments on complex social issues.
Methods: Authors used an automated framework powered by large language models to predict outcomes of 276 field experiments drawn from economics literature.
Key Findings: The prediction accuracy of large language models for outcomes of field experiments addressing various human behaviors.
Citations: 68
Sample Size: 276
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Authors: J Zou, K Vodrahalli
Year: 2024
Published in: ArXiv
Institution: Stanford University
Research Area: Human-AI Interaction in Artistic Creations
Discipline: Artificial Intelligence, Human-Computer Interaction (HCI)
Citations: 12
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Authors: A Berke, R Mahari, A Pentland, K Larson
Year: 2024
Published in: Proceedings of the ACM ..., 2024 - dl.acm.org
Institution: Stanford's CodeX Center, Harvard Law School, MIT Media Lab, Stanford Institute for Human-Centered AI, The Larson Institute, Massachusetts Institute of Technology, Stanford University
Research Area: Crowdsourcing, Transparency, Human-Computer Interaction (HCI) in Social Science Research
Discipline: Computational Social Science, Human-Computer Interaction (HCI)
DOI: https://dl.acm.org/doi/abs/10.1145/3687005
Citations: 9
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Authors: E Jahani, B Manning, J Zhang, H TuYe, M Alsobay, C Nicolaides, S Suri, D Holtz
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, Microsoft Research, Stanford University, University of California Berkeley, University of Cyprus, University of Maryland
Research Area: Human-AI Interaction, Generative AI, Prompt Engineering
Discipline: Artificial Intelligence, focusing on Human-AI Interaction, Generative AI
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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
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Authors: K Zhou,JD Hwang, X Ren,M Sap
Year: 2024
Published in: ArXiv
Institution: Allen Institute for AI, Carnegie Mellon University, Stanford University, University of Southern California
Research Area: LLM Reliability and Uncertainty Quantification, Reinforcement Learning from Human Feedback (RLHF), LLM
Discipline: Artificial Intelligence