Ai Ethics: Academic Discipline — Prolific Citations Library

Explore 22 peer-reviewed papers in Ai Ethics (2023–2025). Academic research using Prolific for high-quality human data collection.

This page lists 22 peer-reviewed papers in the discipline of Ai Ethics in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 22)

People Overtrust AI-Generated Medical Advice despite Low Accuracy

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
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
Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach

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
Humane Artificial Intelligence: Psychological, Social, and Ethical Dimensions

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
ACM Journal on Responsible Computing | Volume 2, Number 4

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
Influence of believed AI involvement on the perception of digital medical advice

Authors: M Reis, F Reis, W Kunde

Year: 2024

Published in: Nature Medicine, 2024 - nature.com

Institution: University of Cambridge, Julius Maximilians Universität

Research Area: AI in Healthcare, Medical Ethics, Cognitive Psychology, Human-Computer Interaction (HCI) in Medicine

Discipline: AI in Healthcare, Medical Ethics, Cognitive Psychology

The study found that medical advice labeled as being sourced from AI (or AI supervised by humans) is perceived as less reliable and empathetic compared to advice labeled as originating solely from a human physician, resulting in reduced willingness to follow such advice.

Methods: Two preregistered studies were conducted where participants were presented with identical medical advice scenarios but with manipulated labels for the advice source ('AI', 'human physician', 'human+AI').

Key Findings: Participants' perceptions of reliability, empathy, and willingness to follow medical advice based on the perceived source.

Citations: 78

Sample Size: 2280
Employees adhere more to unethical instructions from human than AI supervisors: Complementing experimental evidence with machine learning

Authors: L Lanz, R Briker, FH Gerpott

Year: 2024

Published in: Journal of Business Ethics, 2024 - Springer

Institution: University of Lausanne, University of Neuchâtel, University of Bern

Research Area: AI Ethics, Organizational Behavior, Supervisory Influence in the Workplace

Discipline: Business Ethics, Organizational Behavior, Artificial Intelligence Ethics

Employees are less likely to adhere to unethical instructions from AI supervisors compared to human supervisors, partly due to perceived differences in 'mind' and individual characteristics like compliance tendency and age.

Methods: The study employed four experiments using causal forest and transformer-based machine learning algorithms, as well as pre-registered experimental manipulations to evaluate employee behavior towards unethical instructions from AI and human supervisors.

Key Findings: Adherence to unethical instructions from AI versus human supervisors; mediating role of perceived mind and moderating factors like compliance tendency and age.

DOI: https://doi.org/10.1007/s10551-023-05393-1

Citations: 72

Sample Size: 1701
Human participants in AI research: Ethics and transparency in practice

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
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
Surveys considered harmful? Reflecting on the use of surveys in AI research, development, and governance

Authors: M Tahaei, D Wilkinson, A Frik, M Muller

Year: 2024

Published in: Proceedings of the ..., 2024 - ojs.aaai.org

Institution: University of Cambridge, University of Bath, University of Amsterdam, Amazon

Research Area: AI Ethics, Survey Methods, AI Governance

Discipline: AI Ethics, Governance

DOI: https://doi.org/10.1609/aies.v7i1.31734

Citations: 11
Fairness in crowdwork: Making the human AI supply chain more humane

Authors: M Gonzalez-Cabello, A Siddiq, CJ Corbett, C Hu

Year: 2024

Published in: Business Horizons, 2024 - Elsevier

Research Area: Crowdwork Fairness, Human-AI Supply Chain, Business Ethics, Management

Discipline: Business Ethics, Management

DOI: https://doi.org/10.1016/j.bushor.2024.09.003

Citations: 7
Annotator in the Loop: A Case Study of In-Depth Rater Engagement to Create a Prosocial Benchmark Dataset

Authors: S Schmer-Galunder, R Wheelock, Z Jalan

Year: 2024

Published in: Proceedings of the ..., 2024 - ojs.aaai.org

Institution: Google DeepMind, Google, Accenture, Amazon

Research Area: AI Ethics and Prosocial Data Annotation

Discipline: Artificial Intelligence, Ethics, Behavioral Science

DOI: https://doi.org/10.1609/aies.v7i1.31726

Citations: 3
Attributions toward artificial agents in a modified Moral Turing Test

Authors: Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo

Year: 2024

Published in: Nature

Institution: Duke University, ETH Zurich, Georgia State University

Research Area: Moral Responsibility, Agency in AI, Human-AI Moral Interaction

Discipline: Artificial Intelligence Ethics
Strong and weak alignment of large language models with human values

Authors: Mehdi Khamassi, Marceau Nahon1 and Raja Chatila

Year: 2024

Published in: ArXiv

Institution: Sorbonne University

Research Area: AI Alignment, AI Ethics, Computational Cognition

Discipline: Artificial Intelligence, Ethics, Computational Cognition
Applicants' fairness perceptions of algorithm-driven hiring procedures

Authors: M Lavanchy, P Reichert, J Narayanan

Year: 2023

Published in: Journal of Business ..., 2023 - Springer

Institution: Hong Kong Polytechnic University, International Institute for Management Development

Research Area: Applicants' Fairness Perceptions, Algorithm-Driven Hiring, Business Ethics

Discipline: Business Ethics, Human-Computer Interaction (HCI)

DOI: https://doi.org/10.1007/s10551-022-05320-w

Citations: 107
Data subjects' perspectives on emotion artificial intelligence use in the workplace: A relational ethics lens

Authors: S Corvite, K Roemmich, TI Rosenberg

Year: 2023

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

Institution: S Corvite: University of Washington, K Roemmich: University of Washington, TI Rosenberg: University of Washington

Research Area: AI Ethics

Discipline: Artificial Intelligence Ethics

DOI: https://doi.org/10.1145/3579600

Citations: 79
Are natural faces merely labelled as artificial trusted less?

Authors: B Liefooghe, M Oliveira, LM Leisten

Year: 2023

Published in: Collabra ..., 2023 - online.ucpress.edu

Institution: Ghent University, ISCTE-IUL, CIS-IUL, Universidade de Lisboa

Research Area: AI Ethics, Trust in AI, Social Evaluation of AI, AI Evaluation

Discipline: Social Science, AI Ethics

DOI: https://doi.org/10.1525/collabra.73066

Citations: 14
Human-in-the-loop fairness: Integrating stakeholder feedback to incorporate fairness perspectives in responsible AI

Authors: E Taka, Y Nakao, R Sonoda, T Yokota, L Luo

Year: 2023

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

Institution: University of Glasgow, Fujitsu Limited

Research Area: Responsible AI, AI Fairness, Human-in-the-Loop (HITL)

Discipline: Artificial Intelligence, Ethics

DOI: https://doi.org/10.48550/arXiv.2312.08064

Citations: 7
Humans forgo reward to instill fairness into AI

Authors: LS Treiman, CJ Ho, W Kool

Year: 2023

Published in: Proceedings of the AAAI Conference on ..., 2023 - ojs.aaai.org

Institution: Massachusetts Institute of Technology, Yale University

Research Area: AI Ethics, Human-AI Interaction, Fairness in Machine Learning Training

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

DOI: https://doi.org/10.1609/hcomp.v11i1.27556

Citations: 6
Stop agonising over informed consent when researchers use crowdsourcing platforms to conduct survey research

Authors: J Lewis, V Dranseika, S Holm

Year: 2023

Published in: Clinical Ethics, 2023 - journals.sagepub.com

Institution: University of Manchester, Jagiellonian University

Research Area: Research Ethics in Crowdsourcing, Survey Methodology

Discipline: Research Ethics