Algorithm Validation as a Policy Audit: Evidence from Race-blind Charging

arXiv:2609.13174v1 Announce Type: new Abstract: California recently required all prosecutors in the state to conduct a "race-blind charging" decision by reviewing case documents in which selected race-related proxies have been redacted. We validate bc2, an open-source, LLM-based algorithm that we developed to automate this redaction and that was used to facilitate race-blind review in more than 119,000 real-world cases in 2025. We evaluate two distinct questions: whether bc2 faithfully implements the state's requirements and whether those requirements, even when faithfully implemented, advance the goal of race-blind decision-making. To do so, we draw on a corpus of nearly 5,000 real-world police reports that we assembled from jurisdictions across the United States. Under a stringent document-level measure, we find that the latest version of bc2 faithfully implements the legal mandate on 96.7% of narratives in our sample. This performance represents a substantial improvement over earlier versions of bc2 and exceeds that of leading open-source redaction methods. Our validation also shows that California's mandate misses key proxies for race, including location information. Redacting these additional proxies beyond those covered by the state mandate, as bc2 does, eliminates 43.1% of the predictive signal that remains after compliance with the mandate. These findings show that validation can do more than assess technical compliance: it can also improve algorithms and help policymakers achieve underlying policy goals.

Algorithm validation improves technical compliance and policy goals.

This study shows an AI tool helping implement race-blind prosecution, which can improve fairness. However, limitations remain as the state's mandate misses key proxies, so systemic bias is not fully addressed. The impact is positive but partial.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   64d7c835-16c3-4a7a-9fa1-287261da540a
           https://arxiv.org/abs/2609.13174
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EVIDENCE   3 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      -15  (Mixed or uncertain)
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CONFIDENCE 75%