Browse 10 peer-reviewed papers from Microsoft spanning Human-AI Interaction, Trust in AI (2018–2025). Research powered by Prolific's high-quality participant data.
This page lists 10 peer-reviewed papers from researchers at Microsoft in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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
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Authors: P Cooper, A Lim, J Irons, M McGrath, H Jarvis
Year: 2025
Published in: Proceedings of the ..., 2025 - dl.acm.org
Institution: Microsoft Research, Massachusetts Institute of Technology, University of Washington
Research Area: Human-AI Interaction, Trust in AI
Discipline: Human-Computer Interaction (HCI)
Trust in AI dynamically influences users' reliance on AI advice during a deepfake detection task, with no significant impact observed from the timing of AI advice delivery.
Methods: Researchers conducted an online study with participants performing a deepfake detection task, comparing performance across conditions where AI advice was provided either concurrently with decisions or after an initial evaluation. Computational modeling was used to analyze trust dynamics.
Key Findings: Impact of AI advice and its timing on task performance, and the dynamic role of user trust in AI based on expectations of its ability.
DOI: https://dl.acm.org/doi/10.1145/3706599.3719870
Citations: 1
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Authors: Mohit Chandra, Javier Hernandez, Gonzalo Ramos, Mahsa Ershadi, Ananya Bhattacharjee, Judith Amores, Ebele Okoli, Ann Paradiso, Shahed Warreth, Jina Suh
Year: 2025
Published in: ArXiv
Institution: Georgia Institute of Technology, Microsoft Research, University of Toronto, Microsoft
Research Area: Social and Emotional Human-AI Interaction, Psychosocial Effects of AI Chatbot Use
Discipline: Social Science
This study found that active use of conversational AI tools significantly increased perceived attachment, empathy, and emotional support from AI, while showing the potential for improving social and emotional interactions with proper safeguards.
Methods: Participants were divided into two groups: one group used conversational AI tools actively (AU, n=89), and a baseline group used AI and the internet regularly (BU, n=60). Emotional and social interaction measures were tracked over five weeks.
Key Findings: Perceived attachment towards AI, AI empathy, comfort in using AI for emotional support, stress management, and discussion of personal topics.
Sample Size: 149
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Authors: JD Lomas, W van der Maden
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: Delft University of Technology, Microsoft Research
Research Area: Affective Computing, Human-AI Interaction, Image Generation
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2405.18510
Citations: 5
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Authors: E Jahani, B Manning, J Zhang, H TuYe, M Alsobay, C Nicolaides, S Suri, D Holtz
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, Microsoft Research, Stanford University, University of California Berkeley, University of Cyprus, University of Maryland
Research Area: Human-AI Interaction, Generative AI, Prompt Engineering
Discipline: Artificial Intelligence, focusing on Human-AI Interaction, Generative AI
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Authors: Yang Trista Cao, Anna Sotnikova, Jieyu Zhao, Linda X. Zou, Rachel Rudinger, Hal Daumé III
Year: 2024
Published in: ArXiv
Institution: Microsoft Research, University of Maryland
Research Area: Multilingual Bias, Social Science, LLM, AI Bias
Discipline: Artificial Intelligence, Social Science, Large Language Models
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Authors: Eunhae Lee, Pat Pataranutaporn, Judith Amores, Pattie Maes
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, Microsoft Research, MIT Media Lab
Research Area: Human-AI Interaction, Cognitive Biases, Psychological Factors in AI Adoption, Trust in AI, AI Credibility
Discipline: Psychology, Artificial Intelligence
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Authors: E Peer, D Rothschild, A Gordon, E Damer
Year: 2022
Published in: Behavior Research Methods, 2022 - Springer
Institution: The Hebrew University of Jerusalem, Microsoft Research, Prolific
Research Area: Online Behavioral Research, Data Quality, Research Methods
Discipline: Computational Social Science, Behavioral Research
Citations: 2112
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Authors: S Trott
Year: 2021
Published in: Open Mind, 2024 - direct.mit.edu
Institution: Stanford University, Microsoft Research
Research Area: LLMs in Social Science Research, Crowdworking, Human Behavior Simulation
Discipline: Artificial Intelligence, Social Science, Information Systems
Citations: 22
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Authors: JW Vaughan
Year: 2018
Published in: Journal of Machine Learning Research, 2018 - jmlr.org
Institution: Microsoft Research
Research Area: Crowdsourcing for Machine Learning Research, including data generation, model evaluation, hybrid intelligence systems, behavioral experiments.
Discipline: Machine Learning
Citations: 264