Prediction and Punishment: Cross-Disciplinary Workshop on Carceral AI
Center for Philosophy of Science
University of Pittsburgh

This cross-disciplinary workshop will provide an interactive meeting point for researchers to address the expanding use of AI in criminal legal contexts. We use the term ‘carceral AI’ to refer to a broad class of algorithmic and data-driven practices implicated in the control and incarceration of people. Examples include predictive policing, facial recognition, recidivism risk assessment instruments, automatic license plate readers, border surveillance systems, biometric databases, electronic monitoring, and audio gunshot locators. Such technologies are often introduced as ‘smart’, ‘evidence-based’, or ‘data-driven’ reforms that claim to reduce bias and increase efficiency, such as ‘evidence-based’ sentencing and ‘smart’ borders. In practice, however, AI systems can interact in complicated ways with existing social and legal structures, reinforce or mask existing structural injustices, and expand the reach of carceral systems under the guise of scientific rigor. Participants in this workshop are invited to explore how such technologies both inform and interact with topics including incarceration, policing, migration, privatization, surveillance, racial and gender justice, and resistance. We welcome contributions from civil society organizations and academic researchers from disciplines including but not limited to philosophy, law, and the social sciences. Participants will be invited to contribute to a special report on carceral AI.

Keynote Speakers:
Shakeer Rahman, Stop LAPD Spying Coalition
Megan Stevenson, University of Virginia Law School
Pablo Nuñez, Centro de Estudos de Segurança e Cidadania (CESeC)
Gabbrielle Johnson, Claremont McKenna College, Department of Philosophy

• The relation between prediction and discovery on the one hand, and explanation and understanding on the other, in fields of science that heavily rely on ML methods

• The key issues in identifying genuine discoveries and stable predictions by ML systems

• Core conceptions of “explanation” involved in the field of eXplainable AI (XAI), and their relation to philosophical theories of understanding and explanation

• Present limitations associated with ML’s predictive power and what may be needed to overcome them

• The connection between ML and traditional scientific means for prediction and discovery, such as theories, models, and experiments

• Our present understanding of ML itself and its limitations 

• Life Sciences

• Machine Learning Theory

• Philosophy

• Physics / Astronomy