Ai Systems: Research Area — Prolific Citations Library

Discover 12 peer-reviewed studies in Ai Systems (2022–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 12 peer-reviewed papers in the research area of Ai Systems in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (12 of 12)

Once One Fails, All Are Suspect: Understanding Error Generalization in AI

Authors: L Dai, Z Wang, L Chen, J Jin

Year: 2026

Published in: 2026•scholarspace.manoa.hawaii.edu

Institution: Shanghai International Studies University

Research Area: Socio-Economic Impacts of AI, Algorithmic Systems

Discipline: Computer Science, Artificial Intelligence

AI errors lead to broader negative generalizations about other AI systems compared to human errors, largely due to perceptions of AI's inflexibility and inability to learn from mistakes.

Methods: Conducted four one-factor experiments across distinct contexts to compare human responses to AI errors and human errors.

Key Findings: Generalization of error perceptions from one AI system to others, and psychological mechanisms driving this process.
Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations

Authors: S de Jong, V Paananen, B Tag

Year: 2025

Published in: Proceedings of the ACM on ..., 2025 - dl.acm.org

Institution: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University

Research Area: Cognitive Forcing, Human-AI Interaction, AI Explainability (XAI), Decision-Making in AI Systems.

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

Partial explanations encourage critical thinking and reduce user overreliance on incorrect AI suggestions, with performance varying based on individual need for cognition and task difficulty.

Methods: Two experiments were conducted: (1) participants identified shortest paths in weighted graphs, and (2) participants corrected spelling and grammar errors in text, with AI suggestions accompanied by no, partial, or full explanations.

Key Findings: Effectiveness of partial explanations in reducing overreliance on incorrect AI suggestions, and interaction of explanation type with task difficulty and user need for cognition.

DOI: https://doi.org/10.1145/3710946

Citations: 14

Sample Size: 474
Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback: AD Lindström et al.

Authors: A Dahlgren Lindström, L Methnani, L Krause

Year: 2025

Published in: Ethics and Information ..., 2025 - Springer

Institution: Umeå University, Vrije Universiteit Amsterdam

Research Area: AI Alignment, AI Safety, Reinforcement Learning from Human Feedback (RLHF), Sociotechnical Systems

Discipline: Artificial Intelligence, Ethics

The paper critiques AI alignment efforts using RLHF and RLAIF, highlighting theoretical and practical limitations in meeting the goals of helpfulness, harmlessness, and honesty, and advocates for a broader sociotechnical approach to AI safety and ethics.

Methods: Sociotechnical critique of RLHF techniques with an analysis of theoretical frameworks and practical implementations.

Key Findings: The alignment of AI systems with human values and the efficacy of RLHF techniques in achieving the HHH principle (helpfulness, harmlessness, honesty).

DOI: https://doi.org/10.1007/s10676-025-09837-2

Citations: 14
The levers of political persuasion with conversational artificial intelligence

Authors: K Hackenburg, BM Tappin, L Hewitt, E Saunders

Year: 2025

Published in: Science, 2025 - science.org

Institution: London School of Economics and Political Science, Stony Brook University

Research Area: Political Persuasion with Conversational AI, LLM, Factual Accuracy in AI Systems.

Discipline: Political Science, Computational Social Science

This Science paper shows that conversational AI chatbots can systematically influence political opinions at scale, and that techniques like post-training and prompting make them far more persuasive—but that increased persuasion is tied to reduced factual accuracy in what the AI says.

Citations: 12
Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots

Authors: Z Ashktorab, A Buccella, J D'Cruz, Z Fowler, A Gill, KY Leung, PD Magnus, J Richards

Year: 2025

Published in: arXiv preprint arXiv:2507.02745, 2025•arxiv.org

Institution: IBM Research, University at Albany

Research Area: Human–AI interaction, AI systems evaluation, UX, User Experience

Discipline: Computer Science, Human–Computer Interaction (HCI)

In a preregistered study with 162 participants, people generally prefer explanatory apologies from LLM chatbots over rote or purely empathic ones—though in biased error scenarios empathic apologies are sometimes favored—highlighting the complexity of designing chatbot apologies that effectively repair trust.

DOI: https://doi.org/10.48550/arXiv.2507.02745

Citations: 1
The consequences of AI training on human decision-making

Authors: LS Treiman, CJ Ho, W Kool

Year: 2024

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

Institution: Massachusetts Institute of Technology, Yale University, Washington University in St. Louis

Research Area: AI Ethics, Behavioral Economics, Decision-Making in AI Systems

Discipline: Artificial Intelligence, Behavioral Science

People alter their behavior when they know their actions will train AI, leading to unintentional habits and biased training data for AI systems.

Methods: Five studies were conducted using the ultimatum game; participants were tasked with deciding on monetary splits proposed by either humans or AI, with some informed their decisions would train the AI.

Key Findings: Behavioral changes in participants when training AI, persistence of these changes over time, and implications for AI training bias.

DOI: https://doi.org/10.1073/pnas.2408731121

Citations: 13
Fairness in algorithmic management: Bringing platform-workers into the fold

Authors: N Jabagi, AM Croteau, L Audebrand, J Marsan

Year: 2024

Published in: 2024 - aisel.aisnet.org

Institution: FSA ULaval, JMSB, Concordia

Research Area: Algorithmic management, Perceived fairness, Platform work, Information Systems

Discipline: Information Systems, Social Science

DOI: https://aisel.aisnet.org/hicss-57/cl/ai_and_future_work/5

Citations: 8
Utilizing human behavior modeling to manipulate explanations in AI-assisted decision making: the good, the bad, and the scary

Authors: Z Li, M Yin

Year: 2024

Published in: Advances in Neural Information Processing ..., 2024 - proceedings.neurips.cc

Institution: Purdue University

Research Area: Human Behavior Modeling, Explainable AI, Decision Making in AI systems.

Discipline: Artificial Intelligence, Behavioral Science

DOI: https://doi.org/10.52202/079017-0163

Citations: 7
Motivation Matters: Challenges and Pit-Falls of Crowdsourced Online Studies

Authors: E Becks, V Matkovic, T Weis

Year: 2024

Published in: 2025 IEEE International Conference on ..., 2025 - computer.org

Institution: University of Stuttgart, University of Applied Sciences Offenburg, University of Hohenheim

Research Area: Crowdsourced Online Studies, Human-Computer Interaction (HCI) in AI Systems, Behavioral Research Methodology

Discipline: Human-Computer Interaction (HCI)
Navigating User Experience of ChatGPT-based Conversational Recommender Systems: The Effects of Prompt Guidance and Recommendation Domain

Authors: Yizhe Zhang, Yucheng Jin, Li Chen, Ting Yang

Year: 2024

Published in: ArXiv

Institution: Department of Computer Science China, Duke Kunshan University, Hong Kong Baptist University

Research Area: User Experience (UX), Conversational AI, Recommender Systems

Discipline: Computer Science
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
Intersectional experiences of unfair treatment caused by automated computational systems

Authors: T Van Nuenen, J Such, M Cote

Year: 2022

Published in: Proceedings of the ACM on human ..., 2022 - dl.acm.org

Institution: University of Surrey, King’s College London, Tilburg University, University of Amsterdam

Research Area: Intersectional Fairness, Automated Systems, Social Computing

Discipline: Computational Social Science

Citations: 23