Browse 8 peer-reviewed papers from Yale University spanning AI Ethics, Responsible AI (2020–2025). Research powered by Prolific's high-quality participant data.
This page lists 8 peer-reviewed papers from researchers at Yale University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: A Qian, R Shaw, L Dabbish, J Suh, H Shen
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Carnegie Mellon University, University of Pittsburgh, University of Utah, Yale School of Medicine, Yale University
Research Area: Responsible AI, Content Moderation, Risk Disclosure, Worker Well-being in Human-Computer Interaction (HCI).
Discipline: Computational Social Science, Human-Computer Interaction (HCI)
The paper examines how task designers approach well-being risk disclosure in Responsible AI (RAI) content work, highlighting a need for better frameworks to communicate such risks effectively.
Methods: Interviews were conducted with 23 task designers from academic and industry sectors to gather insights on risk recognition, interpretation, and communication practices.
Key Findings: How task designers recognize, interpret, and communicate well-being risks in RAI content work.
Citations: 1
Sample Size: 23
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Authors: Ali Merali
Year: 2025
Published in: ArXiv
Institution: Yale University
Research Area: LLM-Assisted Economic Productivity, Consulting, Data Analysis
Discipline: Economics, Artificial Intelligence
The paper identifies scaling laws linking LLM training compute to professional productivity gains, showing an 8% annual reduction in task time influenced by both compute and algorithmic advances, but with uneven impacts across task types.
Methods: A preregistered experiment involving professional tasks completed by consultants, data analysts, and managers using 13 different LLMs.
Key Findings: Economic productivity impacts of LLMs in professional settings, time savings across task categories, and contribution of compute versus algorithmic progress.
Citations: 1
Sample Size: 500
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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
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Authors: M Shin, J Kim
Year: 2024
Published in: Available at SSRN 4725351, 2024 - researchgate.net
Institution: Massachusetts Institute of Technology, Yale University
Research Area: Linguistic Feature Alignment, Persuasion, LLM
Discipline: Artificial Intelligence, Computational Social Science
Citations: 11
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Authors: Yuxin Wang♣ Xiaomeng Zhu◆ Weimin Lyu♠∗ Saeed Hassanpour♣ Soroush Vosoughi♣
Year: 2024
Published in: ArXiv
Institution: Department of Computer Science Dartmouth College, Stony Brook University, Yale University
Research Area: Natural Language Processing, Computational Linguistics
Discipline: Natural Language Processing
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Authors: O Raccah, P Chen, TM Gureckis, D Poeppe, VA Vo
Year: 2024
Published in: Nature
Institution: Intel Labs, New York University, Yale University
Research Area: Cognitive Psychology, Memory Research, Natural Language Processing (NLP)
Discipline: Psychology, Artificial Intelligence
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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
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Authors: A Bergman, A Chinco, SM Hartzmark
Year: 2020
Published in: Start-Up Guide and ..., 2020 - papers.ssrn.com
Institution: Yale School of Management, University of Miami School of Business, New York University, Columbia University
Research Area: Survey Methodology, Experimental Design, Social Science Research Methods
Discipline: Social Science Research Methods
Citations: 34