About

Mona Sloane, Ph.D., is an Assistant Professor of Data Science and Media Studies at the University of Virginia (UVA). As a sociologist, she studies the intersection of technology and society, specifically in the context of AI design, use, and policy. At UVA, she is a Faculty Co-Lead in the Digital Technology and Democracy Lab at the Karsh Institute of Democracy, Affiliated Faculty with the Department of Women, Gender and Sexuality, and Faculty Affiliate with the Thriving Youth in a Digital Environment (TYDE) research initiative. Additionally, she convenes the Co-Opting AI series, a public speaker series focused on all aspects of AI technology and its application, and serves as the editor of the Co-Opting AI book series at the University of California Press as well as the Technology Editor for Public Books. Her book Predicted: How AI Is Restructuring Society will be released in the Spring of 2026 by the University of California Press. Mona’s current research focuses on AI’s influence on the organization of social life, applied AI accountability and transparency, and AI policy and governance. She runs Sloane Lab at UVA, which leads interdisciplinary research and applied work on responsible AI, public scholarship, and technology policy, including projects on AI auditing, transparency, procurement, hiring, public education, investigative journalism, and governance. Her previous projects include co-chairing the NASEM project on Human and Organizational Factors in AI Risk Management, organizing the public interest technology convention A BETTER TECH, and investigating digital public spaces in NYC during the pandemic. Mona has been recognized as a leading figure in AI ethics, being added to the “100 Brilliant Women in AI Ethics Hall of Fame” and named a NYU Faculty Honoree. She holds a Ph.D. from the London School of Economics and Political Science and has completed fellowships at UC Berkeley, the University of Cape Town, and the Weizenbaum Institute Berlin. Before joining UVA, she was a Research Assistant Professor at NYU’s Tandon School of Engineering, a Senior Research Scientist at the NYU Center for Responsible AI, and the Founding Director of the *This Is Not A Drill* program. Mona is a frequent public speaker and commentator, contributing to outlets such as The Guardian, MIT Technology Review, Nature, and others.

Research topics

• Computer Science
• Machine Learning
• Psychology
• Clinical psychology
• Accounting
• Natural Language Processing
• Social psychology
• Applied psychology
• Artificial Intelligence
• Philosophy
• Data science
• Business
• Speech recognition
• Linguistics

Selected publications

• Datasheets for machine learning sensors

  AI Magazine · 2026-01-31

  articleOpen access

  Abstract Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These “ML sensors” enable context‐sensitive, real‐time data collection and decision‐making across diverse applications ranging from anomaly detection in industrial settings to wildlife tracking for conservation efforts. As such, there is a need to provide transparency in the operation of such ML‐enabled sensing systems through comprehensive documentation. This is needed to enable their reproducibility, to address new compliance and auditing regimes mandated in regulation and industry‐specific policy, and to verify and validate the responsible nature of their operation. To address this gap, we introduce the datasheet for ML sensors framework. We provide a comprehensive template, collaboratively developed in academia—industry partnerships, that captures the distinct attributes of ML sensors, including hardware specifications, ML model and dataset characteristics, end‐to‐end performance metrics, and environmental impacts. Our framework addresses the continuous streaming nature of sensor data, real‐time processing requirements, and embeds benchmarking methodologies that reflect real‐world deployment conditions, ensuring practical viability. Aligned with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), our approach enhances the transparency and reusability of ML sensor documentation across academic, industrial, and regulatory domains. To show the application of our approach, we present two datasheets: the first for an open‐source ML sensor designed in‐house and the second for a commercial ML sensor developed by industry collaborators, both performing computer vision‐based person detection.

  PublisherOA PDFDOI

• Thinking through Data: How Outliers, Aggregates, and Patterns Shape Perception Thinking through Data: How Outliers, Aggregates, and Patterns Shape Perception, by HerrieMaja Bak. Stanford, CA: Stanford University Press, 2025. 166 pp. $95.00 cloth. ISBN: 9781503641891.

  Contemporary Sociology A Journal of Reviews · 2026-05-01

  article1st authorCorresponding
  PublisherDOI

• Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings

  arXiv (Cornell University) · 2026-04-06

  preprintOpen accessSenior author

  As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.

  PublisherDOI

• The case for stakeholder-driven AI auditing in automatic speech recognition

  Nature Machine Intelligence · 2026-03-16

  article1st authorCorresponding
  PublisherDOI

• A Better Burst

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings

  arXiv (Cornell University) · 2026-04-06

  articleOpen accessSenior author

  As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.

  PublisherOA PDF

• A systematic review of regulatory strategies and transparency mandates in AI regulation in Europe, the United States, and Canada

  Data & Policy · 2025-01-01 · 15 citations

  reviewOpen access1st authorCorresponding

  Abstract In this paper, we provide a systematic review of existing artificial intelligence (AI) regulations in Europe, the United States, and Canada. We build on the qualitative analysis of 129 AI regulations (enacted and not enacted) to identify patterns in regulatory strategies and in AI transparency requirements. Based on the analysis of this sample, we suggest that there are three main regulatory strategies for AI: AI-focused overhauls of existing regulation, the introduction of novel AI regulation, and the omnibus approach. We argue that although these types emerge as distinct strategies, their boundaries are porous as the AI regulation landscape is rapidly evolving. We find that across our sample, AI transparency is effectively treated as a central mechanism for meaningful mitigation of potential AI harms. We therefore focus on AI transparency mandates in our analysis and identify six AI transparency patterns: human in the loop, assessments, audits, disclosures, inventories, and red teaming. We contend that this qualitative analysis of AI regulations and AI transparency patterns provides a much needed bridge between the policy discourse on AI, which is all too often bound up in very detailed legal discussions and applied sociotechnical research on AI fairness, accountability, and transparency.

  PublisherOA PDFDOI

• Wildfire Science and Applications Plan

  2025-01-01

  report

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  PublisherDOI

• The Measured Body

  Issues in Science and Technology · 2025-10-01

  article1st authorCorresponding

  Redesigning motion capture systems to be more representative of real human bodies and movements could make them fairer and more useful for applications including law enforcement and medical diagnostics.

  PublisherDOI

• AI’s Sociological Era

  Social Science Computer Review · 2025-11-07

  articleOpen accessSenior author

  Artificial Intelligence (AI) is a social structure cloaked in technical blackboxes and polarizing narratives. Sociology is the study of society from a structural perspective, delineating the interplays between individuals, organizations, institutions, and cultures. Sociology’s disciplinary lens is necessary to understand AI’s infrastructural force, reflecting and shaping politics, economies, knowledge, and interpersonal spheres. Yet to date, the field of AI research has been dominated by computer science and engineering, underutilizing the explanatory power of sociological theories and methods honed by the discipline over more than a century. Building on a collection of papers that illustrate AI as both a product and driver of social patterns and processes, we demonstrate that the diffusion of AI necessitates sociological analyses now more than ever, positioning sociology at the forefront of AI studies’ next era.

  PublisherDOI

Frequent coauthors

• Emanuel Moss

  15 shared

• Hilke Schellmann

  5 shared

• Julia Stoyanovich

  New York University

  4 shared

• Laura Forlano

  4 shared

• Kanta Dihal

  4 shared

• Seán Ó hÉigeartaigh

  4 shared

• Stephen Cave

  4 shared

• Ken Goldberg

  4 shared

Awards & honors

• 100 Brilliant Women in AI Ethics Hall of Fame (2020)