Dana Calacci
Assistant Professor, College of Information Sciences and Technology, Penn State University
Director, Working Futures Lab
Co-Director, Workers Algorithm Observatory
she/her
I study how data and AI impact communities. My current work is focused on helping communities respond to new platforms and AI systems, while designing future AI systems that are more community-centered, accountable, and equitable. I do this through co-research with communities, building and evaluating new tools, and advocating for legal and policy approaches I believe will help make a future that works.
Recent News
2026-03
Three papers accepted to CHI 2026!
We got three in this year: FairFare, our tool for crowdsourcing rideshare data with organizers; a study with Fred Reiber on how digital technology facilitates union busting; and work with Shomik Jain on sycophancy in LLMs.
2026-01
FareShare accepted to CSCW 2026!
FareShare helps labor organizers estimate lost wages and contest arbitrary AI deactivations.
2025-10
New report with Data & Society
Co-authored The ‘Privacy’ Trap with Minsu Longiaru, Wilneida Negron, and others at Data & Society. We dig into how ‘privacy-preserving AI’ techniques are being used to mask new forms of worker surveillance.
2025-06
Two papers accepted to AIES and COMPASS 2025!
Tianqi Kou's paper on dead zones of accountability in ML research was accepted to AIES, and our work on home care worker movement patterns was accepted to COMPASS.
Research Interests
Data Tools For Workers
In a working reality that is increasingly algorithmically managed, workers, researchers, and advocates need tools to manage data and develop alternate algorithmic futures. We build open-source tools like the Workers' Algorithm Observatory (WAO) and FairFare that let workers crowdsource and analyze data about the algorithms that govern their work.
Crowdsourced AI Audits & Harms
We develop methods for communities to collectively audit algorithmic systems. Our research focuses on crowdsourcing data from users of platforms like Amazon, rideshare apps, and delivery services to understand how algorithms impact different groups, and building the tools that make this possible at scale.
Corporate Surveillance of the Commons
We study the spread and impact of corporate surveillance technologies in everyday life. Our research on Amazon Ring documented how participatory mass surveillance networks racialize and criminalize communities, leading to national policy conversations and media coverage.
Data Rights as Labor Rights
We argue that data rights are labor rights: workers should have collective bargaining power over the data generated by their labor. Our work on collective data governance frameworks has informed labor policy in Colorado, Washington, and New York City.
Recent Contributions
view all →FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Organizers
D Calacci, Varun Rao, Samantha Dalal, Catherine Di, Kok Wei Pua, Andrew Schwartz, Danny Spitzberg, Andrés Monroy-Hernandez
ACM Conference on Human Factors in Computing Systems (CHI), 2026
FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations
Varun Rao, Samantha Dalal, Andrew Schwartz, Amna Liaqat, D Calacci, Andrés Monroy-Hernandez
ACM Conference on Computer-Supported Cooperative Work (Forthcoming) (CSCW), 2026
From Zine to Platform: Tracing Community Values through Designing Alternative Social Media
Ankolika De et. al., D Calacci
ACM Conference on Designing Interactive Systems (DIS), 2026
Interaction Context Often Increases Sycophancy in LLMs
Shomik Jain, Charlotte Park, Matt Viana, Ashia Wilson, D Calacci
ACM Conference on Human Factors in Computing Systems (Forthcoming) (CHI), 2026
Organizing in the Digital Age: Understanding Community, Challenges, and Consequences in Digitally facilitated Labor Organizing
Frederick Reiber, Alishah Chator, D Calacci, Allison McDonald
ACM Conference on Computer-Supported Cooperative Work (CSCW), 2026