Dana Calacci

Assistant Professor
Faculty Co-Hire, Institute for Computational and Data Sciences

E331 Westgate Building
288 Burrowes Road
University Park, PA 16802

Email dcalacci@psu.edu
Phone 814-863-3960
Pronouns she, her, they, them

Areas of Expertise

AI
HCI
Computational Social Science
Data Science
Communities
Law
Policy
AI Ethics

Education

Ph.D., Massachusetts Institute of Technology, 2023
M.S., Massachusetts Institute of Technology
B.S., Northeastern University, 2015

Websites

Dana Calacci

Dr. Dana Calacci studies the socio-technical and legal impacts of datafication and AI on communities, especially worker groups. Through collaborations like the Workers Algorithm Observatory, which she helps direct, she designs and deploys technologies with communities that aim to answer their most pressing questions about the impact of AI, new platforms, and surveillance on their lives.

Dana received her Ph.D. from MIT’s Media Lab in 2023, and a B.S. in computer science from Northeastern University in 2015. She also has experience as a startup co-founder and a mixed-media artist. Her writing and work have appeared or been featured in NPR’s Radiolab, Gizmodo, Wired, Reuters, The Atlantic’s CityLab, The New York Times, and other major publications.

Crowdsourced AI Audits and Understanding AI Harms

While teams of experts audit AI tools in the lab, already-deployed AI systems have risks that are difficult to measure a priori. Working directly with communities to crowdsource data about AI impact is crucial to ensuring future systems are transparent and accountable.

To understand the full risks of deploying AI systems, we also need new, nuanced methods of investigating bias and harms, such as in how models make normative judgments

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.

Data Rights as Labor Rights

While workplaces are increasingly quantified and surveilled, normative and legal notions of data privacy conflict with property, trade secret, and IP law in ways that limit worker data access. We need new analyses and understandings of how international data privacy and AI law should be applied in the labor context.

Commercial Surveillance

The increasing ubiquity of corporate surveillance networks like Amazon's Ring Neighbors network carries real risks to public safety, privacy, and corporate power. Mapping these networks and understanding their impact using data and online experiments is one way to investigate those risks.

Google Scholar

App to connect dancers with partners and resources wins IdeaMakers Challenge

Q&A: DeepSeek AI assistant and the future of AI