Contracting for Fair AI: A Computational Text Analysis of State AI Procurement Contracts

• In Event: AI in the Public Sector: Cross-National Evidence on Bureaucracy, Procurement, and Organizational Change

Abstract

Government use of AI has the potential to transform the implementation of public policy by making it more data driven, personalized, and efficient. Yet, longstanding concerns about the trustworthiness, transparency, and fairness of AI systems are heightened when governments are making consequential decisions about people in areas as diverse as criminal justice, healthcare, and social services. Although state governments are increasingly turning to AI tools, they rarely develop these ``in-house'' and instead contract with private firms. This study examines how states are contracting for AI. Empirically, we draw on a dataset of nearly 700 state AI procurement contracts, and employ computational text analysis techniques to analyze common themes in the contract terms that appear - or do not appear - in state AI procurement. Normatively, we argue that state negotiation of AI procurement contracts can be a promising avenue for building better AI by using the state's negotiating power to insert provisions that are aimed at guaranteeing fairness, transparency, and privacy in addition to predictive accuracy.

Author

• Jae Yeon Kim, Harvard University
  Presenting Author