Justice on Autopilot: Comparing Predictive Litigation & Data Justice in Bay Area Municipal Governments

Justice on Autopilot explores how Bay Area counties and cities are using artificial intelligence and predictive tools to make legal and policy decisions - and what that means for fairness, transparency, and democracy. This project compares how local governments adopt and manage predictive litigation systems, asking a central question: Can technology make justice more equitable, or does it risk automating bias?

Grounded in the principles of data justice, this research sheds light on how public institutions can harness innovation responsibly - ensuring that technology serves people, not the other way around. Through case studies, analysis, and community perspectives, Justice on Autopilot aims to spark conversation, accountability, and action toward a more ethical, digital future in government.

The project’s mission is to compare how data-driven legal technologies are selected, implemented, and used across local governments, while assessing whether they advance efficiency at the expense of data justice, transparency, and community trust. By centering the perspectives of public attorneys, policymakers, and impacted communities, the project aims to inform more responsible, equitable, and human-centered approaches to algorithmic decision-making in local justice systems.

More specifically, the analysis of these Bay Area municipalities will be guided by three guiding indicators: governance frameworks, transparency and accountability, and equity and bias awareness. Click on the button below to download my Capstone prospectus!

Capstone Prospectus

Final Capstone Paper

Capstone Presentation

About the Research

This page introduces the purpose, scope, and significance of the capstone, explaining why predictive litigation and data justice matter for municipal governance and how this research contributes to debates on fairness, transparency, and accountability in the use of public-sector algorithms.

Case Studies

These case studies examine how Bay Area municipal governments are integrating predictive analytics into legal and administrative processes, revealing the tensions between innovation, accountability, and data justice.

Research Methodology

This page outlines the research design and methods used to examine predictive litigation and data justice in Bay Area municipal governments, including document analysis, comparative case review, and policy-focused evaluation to assess governance, equity, and accountability impacts.

Data Analysis & Findings

The key results of the Capstone's comparative analysis of predictive litigation tools and data justice practices across the Bay Area. It synthesizes qualitative and quantitative findings to highlight patterns in algorithmic use, governance impacts, and implications for equity, transparency, and public accountability.

Questions?

Contact Mr. Darris Thomas to get more information on the project at dthomas7@dons.usfca.edu

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