Power structures in defining AI-relevant terms

Investigate how power structures shape the definition of relevant technical and legal terms and their interpretations, including how interpretive frictions are utilized, by whom, and for which purposes.

Background

The paper treats accuracy as a boundary object whose meaning is negotiated between machine-learning and legal communities. It argues that these communities bring different epistemic practices, aims, and interpretive frameworks to technical terms, making disagreements over meaning persistent rather than readily resolvable.

The authors identify the role of power in determining which meanings become authoritative as an unresolved avenue for future research. This problem extends the paper's analysis beyond conceptual disagreement to the institutional and political mechanisms through which particular definitions and interpretations gain influence.

References

What remains open as an interesting and important avenue for future work, is the study of power structures in defining relevant terms and interpretations. How are frictions utilized, by whom for which purpose?

— Law of Large Numbers: Accuracy as Statistical Measure for AI Compliance and Competition  (2608.31018 - Derr et al., 31 Aug 2026) in Section "Lessons Learned -- A Satellite Perspective"