Resentencing in California remains a complex legal challenge despite legislative reforms like the Racial Justice Act (2020), which allows defendants to challenge convictions based on statistical evidence of racial disparities in sentencing and charging. Policy implementation lags behind legislative intent, creating a 'second-chance gap' where hundreds of resentencing opportunities remain unidentified.
Redo.io, an open-source platform, processes 95,000 prison records acquired under the California Public Records Act (CPRA) and generates court-ready statistical evidence of racial bias in sentencing for prima facie and discovery motions. This paper explores the design of an LLM-powered interpretive layer that synthesizes results from statistical methods like Odds Ratio, Relative Risk, and Chi-Square Tests into cohesive narratives contextualized with confidence intervals, sample sizes, and data limitations.
This study evaluates LLM performance using both LLM-as-a-Judge framework and human statisticians. Findings suggest that AI can serve as a powerful descriptive assistant for real-time evidence generation when ethically incorporated in the analysis pipeline.
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