Ai Ethics: Research Area — Prolific Citations Library

Discover 26 peer-reviewed studies in Ai Ethics (2023–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 26 peer-reviewed papers in the research area of Ai Ethics in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 26)

Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs

Authors: C Yuan, B Ma, Z Zhang, B Prenkaj, F Kreuter, G Kasneci

Year: 2026

Published in: arXiv preprint arXiv:2601.08634, 2026•arxiv.org

Institution: Munich Center for Machine Learning, LMU Munich, Technical University of Munich

Research Area: Artificial Intelligence, AI Ethics, AI Alignment, Political Science, Computational Social Science

Discipline: Computer Science, Natural Language Processing (NLP)

This paper examines how large language models’ (LLMs) political outputs shift when you explicitly prime them with different moral values. Instead of just assigning fake personas (like “pretend to be liberal”), the authors condition models to endorse or reject specific moral values (e.g., utilitarianism, fairness, authority). They then measure how those moral primes move the models’ positions in...

DOI: https://doi.org/10.48550/arXiv.2601.08634
What large language models know and what people think they know

Authors: M Steyvers, H Tejeda, A Kumar, C Belem

Year: 2025

Published in: Nature Machine ..., 2025 - nature.com

Institution: University of California Irvine

Research Area: Computational Linguistics, Computational Social Science, AI Ethics, Trust in AI

Discipline: Computational Social Science

LLMs often lead to user overestimation of response accuracy, especially with longer explanations; adjusting explanation styles to align with model confidence improves calibration and discrimination gaps, enhancing trust in AI-assisted decision making.

Methods: Conducted experiments using multiple-choice and short-answer questions to study user confidence versus model-stated confidence; varied explanation length and alignment with model internal confidence.

Key Findings: Calibration gap (human vs. model confidence), discrimination gap (ability to distinguish correct vs. incorrect answers), and effects of explanation style and length on user trust.

Citations: 100
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
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
Biased By Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers

Authors: Liudmila Zavolokina, Kilian Sprenkamp, Zoya Katashinskaya, Daniel Gordon Jones

Year: 2025

Published in: ArXiv

Institution: University of Zurich

Research Area: AI Ethics, AI Bias, News Literacy, Critical Thinking, Computational Social Science

Discipline: Computational Social Science

The study explores leveraging inherent biases in AI to enhance critical thinking in news consumption, proposing strategies such as bias awareness, personalization, and gradual introduction of diverse perspectives.

Methods: Qualitative user study investigating user responses to personalized AI-driven propaganda detection tools.

Key Findings: The effectiveness of AI bias-based strategies in improving critical thinking and news readers’ engagement with diverse perspectives.
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
Why human-AI relationships need socioaffective alignment

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
How do citizens perceive the use of Artificial Intelligence in public sector decisions?

Authors: T Haesevoets, B Verschuere, R Van Severen

Year: 2024

Published in: Government Information ..., 2024 - Elsevier

Institution: Ghent University, KU Leuven

Research Area: Public Sector AI, Citizen Perception, AI Ethics, Transparency

Discipline: Political Science, Public Administration

Citizens in the UK prefer AI to play a supporting role in public sector decisions rather than making decisions autonomously, with greater acceptance for low ideologically-charged contexts.

Methods: Three studies surveying UK respondents on their perceptions of AI involvement in public sector decision-making.

Key Findings: Perception of AI's role in decision-making, its legitimacy compared to human decision-makers, and suitability for various types of decisions.

DOI: https://doi.org/10.1016/j.giq.2023.101906

Citations: 54
First-person fairness in chatbots

Authors: T Eloundou, A Beutel, DG Robinson

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: OpenAI, Google DeepMind, Google, University of Oxford

Research Area: Fairness in LLM, AI Bias, AI Ethics

Discipline: Artificial Intelligence, Social Science

The paper introduces a counterfactual approach to evaluate 'first-person fairness' in chatbots, demonstrating that reinforcement learning can mitigate biases based on demographics across extensive chatbot interactions.

Methods: The study uses a Language Model as a Research Assistant (LMRA) to quantitatively and qualitatively assess biases based on demographics across millions of chatbot interactions, covering 66 tasks in 9 domains and involving two genders and four races. Bias evaluations are corroborated by independent...

Key Findings: Demographic biases in chatbot responses, including harmful stereotypes and response differences by gender and race, across diverse tasks and domains.

DOI: https://doi.org/10.48550/arXiv.2410.19803

Citations: 33

Sample Size: 6000000
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
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
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
Good Intentions, Risky Inventions: A Method for Assessing the Risks and Benefits of AI in Mobile and Wearable Uses

Authors: Marios Constantinides, Edyta Bogucka, Sanja Scepanovic, Daniele Quercia

Year: 2024

Published in: ArXiv

Institution: Nokia Bell Labs

Research Area: AI Ethics, Mobile and Wearable AI, Risk Assessment

Discipline: Artificial Intelligence
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