AI Suppression: E-Discovery Software And Brady
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Keywords

Brady
e-discovery
AI suppression
AI management
AI governance
discovery
evidence
evidence law

How to Cite

Hartline, J., Wexler, R., Shan, L., & Sun, A. (2026). AI Suppression: E-Discovery Software And Brady. Science and Technology Law Review, 27(2). https://doi.org/10.52214/stlr.v27i2.14864

Abstract

Prosecutors regularly rely on AI e-discovery software, known as technology assisted review (TAR) tools, to sort and prioritize digital evidence. These tools implicate constitutional concerns: they can either risk suppressing or help to surface exculpatory and impeachment evidence that prosecutors must disclose under the Brady due process rule. Yet doctrine, agency guidance, and scholarship offer virtually no direction on their use.

This Article examines how TAR affects Brady compliance. Using computer science simulations on synthetic data sets, we show that TAR can either hide or help to expose Brady evidence, depending on how it is configured and the configurations of evidence to which it is applied. From these results we derive three TAR workflow recommendations: prosecutors should run TAR separately for inculpatory and Brady evidence; TAR coding of Brady material should be permitted even when active searching is constitutionally contested; and procurement guidelines should favor flexible classifiers.

Our examination of TAR also highlights unresolved tensions in Brady doctrine: whether liability attaches when the prosecution possesses but does not know about Brady evidence; how Brady interacts with Fourth Amendment privacy protections; and whether Brady should be limited to preventing suppression, as current doctrine states, or expanded into a full duty to assist defense investigations. We argue that Brady liability should apply strictly to all Brady evidence in the control of the prosecution team, regardless of whether anyone on the team knows or has reason to suspect that it exists.

https://doi.org/10.52214/stlr.v27i2.14864
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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2026 Jason Hartline, Rebecca Wexler, Liren Shan, Alec Sun