About

Marshini Chetty is an associate professor in the Department of Computer Science at the University of Chicago, where she directs the Amyoli Internet Research Laboratory (AIR lab). Her research specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. She designs, implements, and evaluates technologies to help users manage various aspects of Internet use, including privacy, security, performance, and costs. Her work often focuses on resource-constrained settings and aims to inform Internet policy. Chetty's research explores the interfaces between people and technologies, with a particular emphasis on understanding and defending against emerging threats in our increasingly computational world. She is dedicated to making the Internet more trustworthy and inclusive by drawing on her expertise in human-computer interaction, privacy, security, and ubiquitous computing.

Research topics

• Computer Science
• Computer Security
• Internet privacy
• Artificial Intelligence
• Physics
• Psychology
• Data science
• Engineering
• Business
• Astronomy
• Social psychology
• Pedagogy

Selected publications

• Governance of AI-Generated Content: A Case Study on Social Media Platforms

  arXiv (Cornell University) · 2026-03-08

  preprintOpen accessSenior author

  Online platforms are seeing increasing amounts of AI-generated content -- text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms -- those that are focused on creativity and knowledge-sharing -- address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.

  PublisherDOI

• A Systematic Review of User Experiments on the Effects of Dark Patterns

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Deceptive/Manipulative Patterns (DMP) are interface designs, also known as “dark patterns,” that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly alter user behavior (with large variance in effect size) and that external interventions have been mostly unsuccessful in mitigating their effects. Lastly, we show that significant correlations between DMP effects and personal characteristics (e.g., age or political affiliation) are uncommon, indicating DMPs similarly affected nearly all populations tested. By summarizing the experimental evidence, we clarify the effects of DMPs, highlight gaps and tensions in the existing experimental literature, and help inform ongoing research and policy directions.

  PublisherDOI

• Governance of AI-Generated Content: A Case Study on Social Media Platforms

  2026-04-13

  articleOpen accessSenior author

  Online platforms are seeing increasing amounts of AI-generated content—text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms—those that are focused on creativity and knowledge-sharing—address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.

  PublisherDOI

• Developmentally Safe Generative AI Environment for Youth

  2026-04-13

  articleOpen accessSenior author

  Generative AI (GenAI) systems such as ChatGPT, Gemini, Character.AI, and LLaMA are becoming tightly woven into youths’ everyday lives. Young people now turn to these tools not only for homework help and creative projects, but also for companionship, identity exploration, and emotionally charged conversations. These always-available, highly responsive systems can support learning and self-expression, yet they also introduce new and poorly understood risks for cognitive, emotional, social, moral, and identity development. At the same time, caregivers, educators, designers, and policymakers often have limited visibility into how youth actually use GenAI and few shared frameworks for supporting developmentally appropriate, safe, and empowering engagement. This workshop aims to critically explore the holistic impact of GenAI on youth and to build a shared, developmentally grounded agenda for research and design. Our goals are to: (1) identify promising research approaches and ethical, participatory methods for studying youth–GenAI interactions in situ; (2) surface open challenges, hidden risks, and unintended impacts of GenAI use across diverse cultures and contexts; (3) explore community-based and longitudinal research models that connect youth, caregivers, educators, designers, and policymakers; (4) co-develop shared infrastructures and evaluation metrics to guide design, education, and policy, such as datasets, toolkits, forums, and indicators of developmental safety and well-being. We will bring together researchers and practitioners from HCI, education, child development, security and privacy, and policy to collaboratively sketch actionable strategies for developmentally safe GenAI environments that balance risk mitigation with youth agency, autonomy, and space for exploration and growth.

  PublisherDOI

• Water On My Block: Reflections on Building A Participatory Artificial Intelligence System For Precision Weather With Scientists and An Urban Community

  2026-04-13

  articleOpen accessSenior author

  Creating accurate hyper-local climate Artificial Intelligence (AI) models requires neighborhood-level weather measurements and community partnerships. In this paper, we describe a three year case study of using a participatory approach to support the creation of hyper-local climate AI models, or what we term “precision weather.” Using participatory design to involve stakeholders in the climate AI pipeline design process i.e., “participatory AI,” we collaborated with a national laboratory and a community organization in a major metropolitan area in the United States, working with community members and scientists. We held interviews, co-design workshops (“Community Cafes”), and created an app for the community to collect flood reports in their neighborhood for advocacy and to contribute data to the AI model pipeline. We discuss our findings, lessons learned, and implications for future participatory projects to support hyper-local climate AI.

  PublisherDOI

• A Systematic Review of User Experiments Measuring the Effects of Dark Patterns

  arXiv (Cornell University) · 2026-03-04

  preprintOpen accessSenior author

  Deceptive/Manipulative Patterns (DMP) are interface designs, also known as ``dark patterns,'' that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly alter user behavior (with large variance in effect size) and that external interventions have been mostly unsuccessful in mitigating their effects. Lastly, we show that significant correlations between DMP effects and personal characteristics (e.g., age or political affiliation) are uncommon, indicating DMPs similarly affected nearly all populations tested. By summarizing the experimental evidence, we clarify the effects of DMPs, highlight gaps and tensions in the existing experimental literature, and help inform ongoing research and policy directions.

  PublisherDOI

• Generative AI Uses and Risks for Knowledge Workers in a Science Organization

  ArXiv.org · 2025-01-27

  preprintOpen accessSenior author

  Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.

  PublisherOA PDFDOI

• "I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products

  ArXiv.org · 2025-06-16

  preprintOpen accessSenior author

  While recent research has focused on developing safeguards for generative AI (GAI) model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies and their enforcement in GAI online tools -- consumer-facing web-based GAI applications. We first analyzed content moderation policies of 14 GAI online tools. While these policies are comprehensive in outlining moderation practices, they usually lack details on practical implementations and are not specific about how users can aid in moderation or appeal moderation decisions. Next, we examined user-experienced content moderation successes and failures through Reddit discussions on GAI online tools. We found that although moderation systems succeeded in blocking malicious generations pervasively, users frequently experienced frustration in failures of both moderation systems and user support after moderation. Based on these findings, we suggest improvements for content moderation policy and user experiences in real-world GAI products.

  PublisherOA PDFDOI

• Understanding User Privacy Concerns of Shared Smart TVs

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen access

  As smart TVs gain popularity, they introduce significant privacy and security concerns due to their extensive data collection and multi-user contexts. This paper investigates user perceptions of privacy concerns regarding both service providers and the multi-user use case in the context of smart TVs. Through in-depth interviews with 22 smart TV users, we found that participants expressed uncertainty about the data collection practices of smart TVs and a desire for clearer communication of such practices. Participants reported discomfort with how their personal information is handled through their smart TVs but felt forced to accept it due to the lack of ability to opt out. Our study also highlights varied privacy concerns when smart TVs are shared in public versus private settings. While participants expressed significantly less concern when sharing smart TVs with acquaintances in private settings, concerns were more prevalent in public settings like hotels and Airbnbs. Based on the findings, we provide recommendations for designers, policymakers, and researchers to improve privacy protection and user experience around smart TVs.

  PublisherDOI

• Generative AI Uses and Risks for Knowledge Workers in a Science Organization

  2025-04-24 · 7 citations

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• TWC SBE: TTP Option: Medium: Collaborative: EPICA: Empowering People to Overcome Information Controls and Attacks

  NSF · $195k · 2016–2019

• SaTC: EDU: Collaborative: Connecting Contexts: Building Foundational Digital Privacy and Security Skills for Elementary School Children, Teachers, and Parents

  NSF · $231k · 2020–2024

Frequent coauthors

• Nick Feamster

  University of Chicago

  31 shared

• Arunesh Mathur

  21 shared

• Rebecca E. Grinter

  Georgia Institute of Technology

  15 shared

• Brennan Schaffner

  12 shared

• Jessica Vitak

  University of Maryland, College Park

  11 shared

• Arvind Narayanan

  11 shared

• Tamara Clegg

  University of Maryland, College Park

  10 shared

• W. Keith Edwards

  Georgia Institute of Technology

  7 shared

Labs

• Amyoli Internet Research LabPI

Education

• Ph.D., Human-Computer Interaction

  University of California, Berkeley

  2009

• M.S., Computer Science

  University of California, Berkeley

  2004

• B.S., Computer Science

  University of California, Berkeley

  2002

Awards & honors

• 2026 Future of Privacy Forum’s 2026 Privacy Papers for Polic…
• 2023 Best Paper Honorable Mention, CHI
• 2021 CAREER Award Best Paper Honorable Mention, CHI
• 2019 10th annual privacy papers for policymakers (PPPM) awar…
• Distinguished paper award, SOUPS