Browse 13 peer-reviewed papers in Review. Discover studies powered by high-quality human data from Prolific.
This page lists 13 peer-reviewed papers classified as Review in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
Authors: S Chaudhari, P Aggarwal, V Murahari
Year: 2025
Published in: ACM Computing ..., 2025 - dl.acm.org
Institution: University of Massachusetts Amherst, Carnegie Mellon University, Princeton University
Research Area: Reinforcement Learning from Human Feedback (RLHF), LLM, RLHF
Discipline: Artificial Intelligence
The paper critically analyzes reinforcement learning from human feedback (RLHF) for large language models (LLMs), emphasizing the importance and limitations of reward models in improving human-aligned AI systems.
Methods: Analyzed RLHF frameworks through reinforcement learning principles; conducted a categorical literature review to identify modeling challenges, assumptions, and framework limitations.
Key Findings: Investigated RLHF's fundamentals, focusing on the role of reward models, implications of design choices in RLHF training algorithms, and underlying issues like generalization errors, model misspecification, and feedback sparsity.
Citations: 117
-
Authors: T Greene, G Shmueli, S Ray
Year: 2025
Published in: Journal of the Association for ..., 2023 - aisel.aisnet.org
Institution: National Tsing Hua University, Copenhagen Business School
Research Area: Information Systems Ethics, Reinforcement Learning for Personalization
Discipline: Information Systems
The paper examines the ethical risks of reinforcement learning-based personalization and proposes three research directions for IS scholars to address its societal implications and inadequacies in existing regulations.
Methods: The study presents a conceptual analysis of emergent features and societal risks associated with reinforcement learning-based personalization and proposes research directions.
Key Findings: Potential harms of reinforcement learning-based personalization, such as reduced autonomy, social and political destabilization, and mass surveillance, alongside the limitations of current data protection laws.
DOI: https://aisel.aisnet.org/jais/vol24/iss6/6
Citations: 33
-
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
-
Authors: MM Karim, S Khan, DH Van, X Liu, C Wang, Q Qu
Year: 2025
Published in: Future Internet, 2025 - mdpi.com
Institution: Chinese Academy of Sciences, Zhejiang University, South-Central Minzu University
Research Area: Artificial Intelligence, Data Annotation, Multi-Agent Systems
Discipline: Artificial Intelligence
The paper reviews the role of AI agents powered by large language models in addressing challenges in data annotation, focusing on architectures, workflows, real-world applications, and future research directions for improving efficiency, scalability, transparency, and bias mitigation.
Methods: Comprehensive review and analysis of AI agent architectures, workflows, applications, and evaluation methods in data annotation across multiple industries.
Key Findings: Capabilities of LLM-driven agents in reasoning, adaptive learning, collaborative annotation, and their impact on quality assurance, cost, scalability, and bias mitigation.
Citations: 10
-
Authors: S Lodoen, A Orchard
Year: 2025
Published in: arXiv preprint arXiv:2505.09576, 2025 - arxiv.org
Institution: Embry–Riddle Aeronautical University, University of Waterloo
Research Area: Reinforcement Learning from Human Feedback (RLHF), Procedural Rhetoric, LLM Persuasion, Ethics
Discipline: Artificial Intelligence, AI Ethics, Social Science
The paper uses procedural rhetoric to analyze how RLHF reshapes ethical, social, and rhetorical dimensions of generative AI interactions, raising concerns about biases, hegemonic language, and human relationships.
Methods: The study conducts a theoretical and rhetorical analysis based on Ian Bogost's concept of procedural rhetoric, examining how RLHF mechanisms influence language conventions, information practices, and social expectations.
Key Findings: Ethical and rhetorical implications of RLHF-enhanced LLMs on language usage, information seeking, and interpersonal dynamics.
DOI: https://doi.org/10.48550/arXiv.2505.09576
Citations: 3
-
Authors: G Riva, BK Wiederhold, P Cipresso
Year: 2025
Published in: ... , Behavior, and Social ..., 2025 - liebertpub.com
Institution: Università Cattolica del Sacro Cuore, University of Genova, Università degli Studi di Milano, Università di Catania
Research Area: AI Ethics, Social and Psychological Dimensions of Artificial Intelligence, Human-Computer Interaction (HCI)
Discipline: Artificial Intelligence Ethics, Psychology, Sociology
The paper addresses the psychological, social, and ethical challenges of integrating AI into daily life and emphasizes the need to design AI systems that uphold human values and well-being.
Methods: The paper conducts an interdisciplinary review of existing research and literature to analyze the psychological, social, and ethical dimensions of AI deployment.
Key Findings: The impact of AI on human behavior, decision-making, and societal values.
DOI: https://doi.org/10.1089/cyber.2025.0202
Citations: 3
-
Authors: J van Grunsven, N Jacobs, BA Kamphorst, M Honauer
Year: 2025
Published in: ACM Journal on, 2025 - dl.acm.org
Institution: University of Texas, Microsoft Research, Google DeepMind, Google, University of Washington, World Economic Forum
Research Area: Ethics and Governance of Computing Research, focused on Responsible Computing, Social Science Research, Artificial Intelligence.
Discipline: Ethics, Governance of Computing Research, AI Ethics
The paper emphasizes the importance of accounting for human vulnerability in the design and analysis of digital technologies, proposing concepts like 'Intimate Computing' to empower individuals in managing their technology-mediated vulnerabilities.
Methods: The study reviews and synthesizes existing literature and frameworks addressing vulnerability in human-technology interactions, including concepts like 'Intimate Computing' and 'Person-Machine Teaming'.
Key Findings: Human vulnerability in the context of digitally-mediated interactions and the role of computing frameworks in addressing them.
Citations: 2
-
Authors: JS Michel, G Sawhney, GP Watson
Year: 2025
Published in: How to Conduct and ..., 2025 - elgaronline.com
Institution: Auburn University
Research Area: Crowdsourcing, Research Methods, Social Science
Discipline: Social Science
Crowdsourcing is a versatile tool leveraging collective intelligence for efficient task completion and has applications across various fields including decentralized finance, blockchain technologies, and IO Psychology research and practice.
Methods: The paper discusses the theoretical and practical applications of crowdsourcing in various domains, referencing prior work and examples such as Wikipedia, crowdfunding platforms, and blockchain networks.
Key Findings: The applications and impact of crowdsourcing in different fields, particularly its role in Industrial-Organizational Psychology for data collection and analysis.
Citations: 1
-
Authors: HR Kirk, I Gabriel, C Summerfield, B Vidgen
Year: 2024
Published in: Humanities and Social ..., 2025 - nature.com
Institution: Oxford Internet Institute, University of Oxford
Research Area: Socioaffective Alignment in Human-AI Relationships, AI Ethics, Behavioral Science
Discipline: Artificial Intelligence, Behavioral Science
The paper emphasizes the need for socioaffective alignment in human-AI relationships to ensure AI systems support human psychological needs rather than exploit them, as interactions with AI transition from transactional to sustained engagement.
Methods: Conceptual analysis of socioaffective dynamics in human-AI interactions, framed through psychological theories and principles.
Key Findings: Exploration of how AI systems impact socioaffective relationships, psychological needs, autonomy, companionship, and human well-being.
DOI: https://doi.org/10.1057/s41599-025-04532-5
Citations: 59
-
Authors: S Du, MT Babalola, P D'cruz, E Dóci
Year: 2024
Published in: Journal of Business ..., 2024 - Springer
Institution: Nottingham University Business School, University of Reading, Oxford Brookes University, University of Portsmouth
Research Area: Crowdsourcing Ethics, Social Sciences, Organizational Behavior
Discipline: Social Science
The paper explores the ethical, societal, and global implications of using crowdsourcing platforms for research, emphasizing the need for fair compensation, transparency, and consideration of global disparities between the Global North and South.
Methods: The paper provides a conceptual analysis and critique of crowdsourcing research practices, focusing on ethical and societal considerations.
Key Findings: Ethical, societal, and global implications of crowdsourcing research practices, including data quality, reporting transparency, fair remuneration, and the role of global disparities.
Citations: 24
-
Authors: E Watson, T Viana, S Zhang
Year: 2024
Published in: AI, 2023 - mdpi.com
Research Area: Behavioral Annotation Tools and Multimodal Data
Discipline: Computer Science
The paper systematically reviews augmented behavioral annotation tools, focusing on their evolution, current state, and application to multimodal datasets and models, highlighting best practices and emerging challenges in safe and ethical annotation for large-scale multimodal systems.
Methods: Systematic literature review analyzing crowd and machine learning-augmented behavioral annotation methods, with cross-disciplinary comparisons and structured synthesis of practices.
Key Findings: Evolution of behavioral annotation tools, their integration with machine learning, emerging trends (e.g., prompt engineering), challenges in large multimodal datasets, and ethical and engineering best practices.
DOI: https://doi.org/10.3390/ai4010007
Citations: 17
-
Authors: KR McKee
Year: 2024
Published in: IEEE Transactions on Technology and Society, 2024 - ieeexplore.ieee.org
Institution: University of Queensland
Research Area: AI Ethics, Human-Computer Interaction (HCI), Research Practice Transparency
Discipline: AI Ethics, Human-Computer Interaction (HCI)
The paper identifies ethical and transparency gaps in AI research involving human participants and proposes guidelines to address these issues, drawing from adjacent fields like psychology and human-computer interaction while recognizing unique challenges in AI contexts.
Methods: Analyzed normative practices by reviewing AI research publications and compared them with ethical standards in adjacent fields such as psychology and HCI.
Key Findings: Ethical practices including ethical reviews, informed consent, participant compensation, and contextual considerations specific to AI research.
DOI: https://ieeexplore.ieee.org/abstract/document/10664609/
Citations: 17
-
Authors: GKM Liu
Year: 2024
Published in: Massachusetts Institute of Technology, 2023 - computing.mit.edu
Institution: Massachusetts Institute of Technology
Research Area: Reinforcement Learning with Human Feedback (RLHF), Human-AI Interaction
Discipline: Artificial Intelligence
The paper explores Reinforcement Learning with Human Feedback (RLHF) as a transformative tool to align AI with human values, mitigate bias, and democratize technology, while emphasizing its societal implications and ethical considerations.
Methods: The paper employs a systematic study of existing and potential societal effects of RLHF, guided by key questions addressing ethical, social, and practical impacts.
Key Findings: The study investigates how RLHF affects information integrity, societal values, social equity, access to AI, cultural relations, industrial transformation, and labor dynamics.
Citations: 17