Algorithm Validation as a Policy Audit: Evidence from Race

Computer Science > Computers and Society [Submitted on 27 Jul 2026] Title:Algorithm Validation as a Policy Audit: Evidence from Race-blind Charging View PDF HTML (experimental)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. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.