Critical AI and Data Studies
The social, embodied, and environmental life of data-driven systems.
Overview
Critical AI and data studies examines what artificial intelligence and data-driven systems do in the world: how they shape labor, education, and institutions; how they distribute benefit and harm; what they cost in energy and material; and how they reorganize human experience and public life.
This work is critical in the scholarly sense (analytic, historical, and evidence-driven) and draws on science and technology studies and the social sciences.
Research directions
- AI and labor analysis.
- Algorithmic bias, inequality, and discrimination in pre-transformer ML systems.
- Fairness and bias in AI language, vision, and multimodal models.
- Surveillance, data, and digital publics.
- Embodiment and human experience under data-driven systems.
- The environmental cost and resource consumption of a data-driven planet.
- Critical histories and futures of AI and data infrastructures.
Canonical readings
A starter shelf.
2021
On the Dangers of Stochastic Parrots
Bender, Gebru, McMillan-Major & Mitchell
The defining critique of large language models' social and environmental harms.
2019
Race After Technology
Ruha Benjamin
Canonical account of how technology encodes racial inequity.
2021
Atlas of AI
Kate Crawford
The definitive treatment of AI's material, labor, and environmental costs.
People in this area