Data sacrifices and the ‘third way’ toward AI: justification and critique in local conflicts over automated surveillance

Abstract

Datafication has become a central concern in debates on artificial intelligence (AI). In these discussions, the European approach to AI is often portrayed as a ‘third way’ that balances public interests with innovation. This article examines parliamentary discourse on a police project in Hamburg, Germany, to show how the seemingly value-pluralistic ‘third way’ compromise is mobilized to justify the extraction of data to test and train an AI system for CCTV surveillance. Drawing on the pragmatist “economy of conventions,” the article introduces the original notion of data sacrifices . The concept allows critical data studies to examine how the diverse sociomaterial costs and reductionist generalizations of formatting the world into data are linked to the dynamics of justification and critique that shape the moral conditions of possibility for data extraction. In the article, Critical Discourse Analysis is used to reconstruct five specific suborders of worth invoked in the parliamentary debate to (de-)legitimize the AI system: security, individual freedom, social justice, automation, and experimentalism. It is demonstrated how the ‘third way’ works as a specific mode of justification in which civic values such as equality and privacy are internalized to legitimize the industrialization of surveillance. Claiming an effective technological compromise for complex moral stakes realigns key political actors and watchdogs in support of data extraction. However, Critical Discourse Analysis also demonstrates how this compromise silences certain civic critics to legitimize sacrifices for AI training. The interplay of internalization and exclusion of critique thereby modifies the moral underpinnings of AI surveillance. The framework provided in this article thus enables researchers to reveal the paradoxical nature of ‘third way’ compromises for data extraction. In the case studied, these paradoxes culminate in the first German law explicitly permitting the police to transfer anonymized and non-anonymized surveillance data to external partners for machine learning purposes.