Computer Science: Research Area — Prolific Citations Library

Discover 8 peer-reviewed studies in Computer Science (2021–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 8 peer-reviewed papers in the research area of Computer Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (8 of 8)

On the conversational persuasiveness of GPT-4

Authors: F Salvi, M Horta Ribeiro, R Gallotti, R West

Year: 2025

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

Institution: EPFL, Fondazione Bruno Kessle, Princeton University

Research Area: Conversational Persuasion of LLM, Human-Computer Interaction (HCI), Behavioral Science, LLM

Discipline: Behavioral Science

GPT-4 can use personalized arguments to be more persuasive in debates, outperforming humans in 64.4% of AI-human comparisons when personalization is applied.

Methods: Preregistered controlled study involving multiround debates with random assignment to conditions focusing on AI-human comparisons, personalization, and opinion strength.

Key Findings: Effectiveness of persuasion by GPT-4, especially when using personalized arguments, compared to humans in debates.

Citations: 65

Sample Size: 900
Large Language Models are often politically extreme, usually ideologically inconsistent, and persuasive even in informational contexts

Authors: N Aldahoul, H Ibrahim, M Varvello, A Kaufman

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: Delft University of Technology, University of Pennsylvania, New York University, King Abdullah University of Science and Technology, Massachusetts Institute of Technology, University of Texas at Austin

Research Area: Artificial Intelligence, Computers and Society, Political Science

Discipline: Artificial Intelligence, Social Science

The study finds that Large Language Models (LLMs) exhibit extreme political views on specific topics despite appearing ideologically moderate overall, and demonstrate a persuasive influence on users' political preferences even in informational contexts.

Methods: Compared 31 LLMs' political biases against benchmarks (legislators, judges, representative voter samples) and conducted a randomized experiment to measure their persuasive impact in informational interactions.

Key Findings: Ideological consistency, political extremity, and persuasive effects of LLMs in information-seeking contexts.

Citations: 7

Sample Size: 31
Towards Strategic Persuasion with Language Models

Authors: Z Cheng, J You

Year: 2025

Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org

Institution: University of Southern California, University of California Berkeley

Research Area: Artificial Intelligence, Computers and Society, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, LLM, RLHF

Discipline: Artificial Intelligence

This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.

Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.

Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.

Citations: 1
Overcoming algorithm aversion: A comparison between process and outcome control

Authors: L Cheng, A Chouldechova

Year: 2024

Published in: Proceedings of the 2023 CHI Conference ..., 2023 - dl.acm.org

Institution: Carnegie Mellon University

Research Area: Human-Computer Interaction (HCI), Algorithm Aversion, Decision Science

Discipline: Human-Computer Interaction (HCI)

Giving users process control by selecting the training algorithm mitigates algorithm aversion, but not by changing input factors, while combined outcome and process control is not more effective than each individually.

Methods: Replication study on outcome control and novel process control conditions tested on MTurk and Prolific platforms.

Key Findings: Impact of outcome control, process control, and combined controls on algorithm aversion mitigation.

DOI: https://doi.org/10.1145/3544548.3581253

Citations: 41
Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use

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
Safeguarding crowdsourcing surveys from ChatGPT with prompt injection

Authors: C Wang, SK Freire, M Zhang, J Wei

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Delft University of Technolog, University of Melbourne

Research Area: Human-Computer Interaction (HCI), Computational Social Science, AI Security

Discipline: Human-Computer Interaction (HCI)

DOI: https://doi.org/10.48550/arXiv.2306.08833

Citations: 18
Towards Effective Human-AI Collaboration

Authors: G He

Year: 2023

Published in: repository.tudelft.nl

Institution: Delft University of Technology

Research Area: Human-AI Collaboration, AI Systems, Appropriate Reliance on AI Systems, Artificial Intelligence, Computer Science

Discipline: Artificial Intelligence, Computer Science
To believe or not to believe? Generative AI and the 'trust dilemma'in charitable and medical crowdfunding

Authors: C Arnold, LZ Xu, K Saffarizadeh

Year: 2021

Published in: Behaviour & Information ..., 2025 - Taylor & Francis

Institution: Northwestern Mutual Data Science Institute, Marquette University

Research Area: Generative AI, Crowdfunding, Trust in AI, Human-Computer Interaction (HCI), Behavioral Science

Discipline: Human-Computer Interaction (HCI), Behavioral Science