At CAT Lab, we work alongside the public in industry-independent research to test people’s concerns about digital technology and evaluate ideas for social change. That work requires us to re-imagine how data systems are designed and engineered.
Learn more about our award-winning innovations in public-interest research engineering below:
Engineering Posts and Pages
Statistical Inference and (In)correctness
GenAI’s deceptively plausible outputs make it dangerously easy to overlook its limitations.
Reliable GenAI and Perpetual Motion
Researchers rushing to adopt AI without understanding the limitations risk committing scientific misconduct.
Visualizing the Algorithm
At CAT Lab, data visualizations and data art build intuition and encourage us to look at online communities from new perspectives.
CivilServant: Citizen Science for Online Communities
Moderators of online communities, often volunteers, work hard to protect their communities from harassment, misinformation, trolls, and other online harms. But which policies and procedures actually work?
Working with Data at CAT Lab
We’ve released a guide for collaborators, students, and anyone who wants to steward research data the CAT Lab way.
How can scientists, technologists, and activist-scholars learn to create positive transformations in our work and our world? And how can […]
How can researchers verify the claims made by technology platforms about data access when the realities of access may depend […]
How can the engineers and data scientists who work on public-interest research offer mutual support in our work to create […]
Designing and Evaluating Research Ethics Systems
As social scientists who work with digital data increasingly set aside informed consent, can designers bring fundamental ethical values back […]
Why We Co-Create Research Systems with Communities
Have you ever gotten a finger squeeze from a pulse oximeter? This device can measure your blood-oxygen without a painful […]
Crowdsourced Audits of Algorithmic Decision-Makers
How can independent researchers reliably detect bias, discrimination, and other systematic errors in software-based decision-making systems? Austin Hounsel, Nick Feamster […]
How can we escape the trap of surveillance capitalism to imagine, achieve, and govern a better internet? Speaking today at […]