About

Suresh Venkatasubramanian is a Professor of Data Science and Computer Science at Brown University. He serves as the Director of the Center for Technological Responsibility, Reimagination, and Redesign, as well as the Deputy Director of the Data Science Institute. His primary research areas include Algorithmic Fairness, with secondary focus on Algorithms and Theory, and Machine Learning. He is involved in teaching courses such as 'So you think you want to govern AI?' in Fall 2026. Dr. Venkatasubramanian is also a leader in socio-technical computing, contributing to the development of responsible and equitable technological systems.

Research topics

• Computer Science
• Economics
• Mathematical economics
• Political Science
• Machine Learning
• Management science
• Risk analysis (engineering)
• Engineering
• Mathematics
• Business
• Epistemology
• Actuarial science
• Philosophy

Selected publications

• A text book on Cyber Security

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-07

  bookOpen access1st authorCorresponding
  PublisherDOI

• The Commodification of AI Sovereignty: Lessons from the Fight for Sovereign Oil

  ArXiv.org · 2026-01-16

  articleOpen accessSenior author

  "Sovereignty" is increasingly a part of national AI policies and strategies. At the same time that "sovereignty" is invoked as a priority for global AI policy, it is also being commodified along the AI stack. Companies now sell "sovereign" AI factories, clouds, and language models to governments, enterprises, and communities -- turning a contested value into a commercial commodity. This shift risks allowing private technology providers to define sovereignty on their own terms. By analyzing the history of sovereignty and parallels in global oil production, this paper aims to open avenues to interrogate the implications of this value's commercialization. The contributions of this paper lie in a disentangling of the facets of sovereignty being appealed to through the AI stack and a case for how analogizing oil and AI can be generative in thinking through what is achieved and what can be achieved through the commodification of AI sovereignty.

  PublisherOA PDF

• A text book on Cyber Security

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-07

  bookOpen access1st authorCorresponding
  PublisherDOI

• How to Stop Playing Whack-a-Mole: Mapping the Ecosystem of Technologies Facilitating AI-Generated Non-Consensual Intimate Images

  Open MIND · 2026-02-04

  preprintSenior author

  The last decade has witnessed a rapid advancement of generative AI technology that significantly scaled the accessibility of AI-generated non-consensual intimate images (AIG-NCII), a form of image-based sexual abuse that disproportionately harms and silences women and girls. There is a patchwork of commendable efforts across industry, policy, academia, and civil society to address AIG-NCII. However, these efforts lack a shared, consistent mental model that clearly situates the technologies they target within the context of a large, interconnected, and ever-evolving technological ecosystem. As a result, interventions remain siloed and are difficult to evaluate and compare, leading to a reactive cycle of whack-a-mole. In this paper, we contribute the first comprehensive AIG-NCII technological ecosystem that maps and taxonomizes 11 categories of technologies facilitating the creation, distribution, proliferation and discovery, infrastructural support, and monetization of AIG-NCII. First, we build and visualize the ecosystem through a synthesis of over a hundred primary sources from researchers, journalists, advocates, policymakers, and technologists. Then, we conduct two detailed walkthroughs to demonstrate the usefulness of the ecosystem in 1) making sense of new AIG-NCII harms using a case study of Grok and 2) mapping a clearer tech policy landscape using U.S. federal law and 63 state laws. We conclude with a vision for future AIG-NCII research that refines the edges of the ecosystem, recommending researchers to study critical relationships between technologies and potential ripple effects from different interventions. Our goal is to produce an AIG-NCII technological ecosystem that provides a clear, shared terminology and framework for stakeholders to move into the future of AIG-NCII prevention with clarity and foresight.

  DOI

• More is not better: Visual uncertainty cues and the fragility of trust calibration in LLM-assisted decision making

  Computers in Human Behavior Artificial Humans · 2026-05-01

  articleOpen access
  PublisherDOI

• How to Stop Playing Whack-a-Mole: Mapping the Ecosystem of Technologies Facilitating AI-Generated Non-Consensual Intimate Images

  ArXiv.org · 2026-02-04

  articleOpen accessSenior author

  The last decade has witnessed a rapid advancement of generative AI technology that significantly scaled the accessibility of AI-generated non-consensual intimate images (AIG-NCII), a form of image-based sexual abuse that disproportionately harms and silences women and girls. There is a patchwork of commendable efforts across industry, policy, academia, and civil society to address AIG-NCII. However, these efforts lack a shared, consistent mental model that clearly situates the technologies they target within the context of a large, interconnected, and ever-evolving technological ecosystem. As a result, interventions remain siloed and are difficult to evaluate and compare, leading to a reactive cycle of whack-a-mole. In this paper, we contribute the first comprehensive AIG-NCII technological ecosystem that maps and taxonomizes 11 categories of technologies facilitating the creation, distribution, proliferation and discovery, infrastructural support, and monetization of AIG-NCII. First, we build and visualize the ecosystem through a synthesis of over a hundred primary sources from researchers, journalists, advocates, policymakers, and technologists. Then, we conduct two detailed walkthroughs to demonstrate the usefulness of the ecosystem in 1) making sense of new AIG-NCII harms using a case study of Grok and 2) mapping a clearer tech policy landscape using U.S. federal law and 63 state laws. We conclude with a vision for future AIG-NCII research that refines the edges of the ecosystem, recommending researchers to study critical relationships between technologies and potential ripple effects from different interventions. Our goal is to produce an AIG-NCII technological ecosystem that provides a clear, shared terminology and framework for stakeholders to move into the future of AIG-NCII prevention with clarity and foresight.

  PublisherOA PDF

• The Commodification of AI Sovereignty: Lessons from the Fight for Sovereign Oil

  arXiv (Cornell University) · 2026-01-16

  preprintOpen accessSenior author

  "Sovereignty" is increasingly a part of national AI policies and strategies. At the same time that "sovereignty" is invoked as a priority for global AI policy, it is also being commodified along the AI stack. Companies now sell "sovereign" AI factories, clouds, and language models to governments, enterprises, and communities -- turning a contested value into a commercial commodity. This shift risks allowing private technology providers to define sovereignty on their own terms. By analyzing the history of sovereignty and parallels in global oil production, this paper aims to open avenues to interrogate the implications of this value's commercialization. The contributions of this paper lie in a disentangling of the facets of sovereignty being appealed to through the AI stack and a case for how analogizing oil and AI can be generative in thinking through what is achieved and what can be achieved through the commodification of AI sovereignty.

  PublisherDOI

• Audit Trails for Accountability in Large Language Models

  Open MIND · 2026-01-28

  preprintSenior author

  Large language models (LLMs) are increasingly embedded in consequential decisions across healthcare, finance, employment, and public services. Yet accountability remains fragile because process transparency is rarely recorded in a durable and reviewable form. We propose LLM audit trails as a sociotechnical mechanism for continuous accountability. An audit trail is a chronological, tamper-evident, context-rich ledger of lifecycle events and decisions that links technical provenance (models, data, training and evaluation runs, deployments, monitoring) with governance records (approvals, waivers, and attestations), so organizations can reconstruct what changed, when, and who authorized it. This paper contributes: (1) a lifecycle framework that specifies event types, required metadata, and governance rationales; (2) a reference architecture with lightweight emitters, append only audit stores, and an auditor interface supporting cross organizational traceability; and (3) a reusable, open-source Python implementation that instantiates this audit layer in LLM workflows with minimal integration effort. We conclude by discussing limitations and directions for adoption.

  DOI

• Audit Trails for Accountability in Large Language Models

  ArXiv.org · 2026-01-28

  articleOpen accessSenior author

  Large language models (LLMs) are increasingly embedded in consequential decisions across healthcare, finance, employment, and public services. Yet accountability remains fragile because process transparency is rarely recorded in a durable and reviewable form. We propose LLM audit trails as a sociotechnical mechanism for continuous accountability. An audit trail is a chronological, tamper-evident, context-rich ledger of lifecycle events and decisions that links technical provenance (models, data, training and evaluation runs, deployments, monitoring) with governance records (approvals, waivers, and attestations), so organizations can reconstruct what changed, when, and who authorized it. This paper contributes: (1) a lifecycle framework that specifies event types, required metadata, and governance rationales; (2) a reference architecture with lightweight emitters, append only audit stores, and an auditor interface supporting cross organizational traceability; and (3) a reusable, open-source Python implementation that instantiates this audit layer in LLM workflows with minimal integration effort. We conclude by discussing limitations and directions for adoption.

  PublisherOA PDF

• Distinguishing Task-Specific and General-Purpose AI in Regulation

  2026-03-03

  articleOpen accessSenior author

  Over the past decade, policymakers have developed a set of regulatory tools to ensure AI development aligns with key societal goals. Many of these tools were initially developed in response to concerns with task-specific AI and therefore encode certain assumptions about the nature of AI systems and the utility of certain regulatory approaches. With the advent of general-purpose AI (GPAI), however, some of these assumptions no longer hold, even as policymakers attempt to maintain a single regulatory target that covers both types of AI.

  PublisherDOI

Recent grants

• BIGDATA: Small: DA: Collaborative Research: From Data to Users: Providing Interpretable and Verifiable Explanations in Data Mining

  NSF · $500k · 2013–2017

• BIGDATA: Collaborative Research: F: Algorithmic Fairness: A Systemic and Foundational Treatment of Nondiscriminatory Data Mining

  NSF · $484k · 2016–2020

Frequent coauthors

• Sorelle A. Friedler

  40 shared

• Carlos Scheidegger

  34 shared

• Jeff M. Phillips

  University of Utah

  27 shared

• John Moeller

  Dartmouth–Hitchcock Medical Center

  17 shared

• Avishek Saha

  Netaji Subhas Open University

  16 shared

• Hal Daumé

  15 shared

• Andrew McGregor

  University of Massachusetts Amherst

  13 shared

• Neal Patwari

  Washington University in St. Louis

  13 shared

Labs

• Socially Responsible Computing @ Brown CSPI

  Develop and implement content on the social impact of technology in CS courses.

Education

• Ph.D., Computer Science

  University of California, Los Angeles

  1996

• M.S., Computer Science

  University of California, Los Angeles

  1993

• B.S., Electrical and Electronics Engineering

  University of Madras

  1991