This Capstone project will contribute to scholarship and practice in several significant ways, bridging academic inquiry with practical application in the domains of public administration, technology governance, and racial justice.

Empirical Contributions to Scholarship

This study will address a critical gap in empirical research on algorithmic systems in local government. While scholars have extensively examined predictive policing and risk assessment tools in criminal sentencing, the deployment of predictive analytics in municipal litigation remains largely unexplored. Yet these systems may affect thousands of cases annually—shaping settlement decisions in civil rights lawsuits, employment disputes, personal injury claims, and other matters with profound consequences for individuals and communities. By documenting how city attorneys, district attorneys, public defenders, and county counsel offices actually use these technologies in practice, this research will provide much-needed empirical grounding to debates that have largely proceeded in the abstract.

Moreover, this study contributes to our understanding of the political economy of algorithmic governance at the local level. Existing research on AI in government tends to focus either on federal agencies or on high-profile applications like predictive policing. Municipal legal offices represent a different institutional context—one characterized by resource constraints, political pressures from elected officials, and direct accountability to local communities. Understanding how these contextual factors shape technology adoption and governance practices illuminates broader patterns about how and why local governments turn to algorithmic systems, and under what conditions those systems are subject to meaningful oversight.

Theoretical and Methodological Contributions

This project will demonstrate the analytical power of data justice as a framework for evaluating algorithmic systems in government settings. Much of the existing literature on algorithmic accountability focuses narrowly on technical metrics—accuracy rates, false positive ratios, disparate impact measurements. While these metrics matter, they cannot capture the full range of ethical and political concerns raised by the deployment of AI in public institutions. Data justice offers a more expansive framework that attends to questions of recognition (whose knowledge and perspectives are valued in system design), participation (who has voice and power in governance decisions), and redistribution (how algorithmic systems distribute benefits and harms across different communities).

By operationalizing data justice principles in a specific empirical context, this study will provide a model for how scholars and practitioners can move beyond technocratic evaluations to ask deeper questions about equity and democracy. The comparative case study approach will reveal whether and how data justice principles can be institutionalized through governance frameworks, procurement processes, and oversight mechanisms. This methodological contribution is particularly important as municipalities across the country grapple with how to govern AI systems responsibly.

Furthermore, this research speaks to ongoing theoretical debates about the relationship between technology and structural inequality. Do algorithmic systems simply replicate existing patterns of bias, or do they transform them in qualitatively new ways? Can technological systems designed for efficiency be redirected toward equity, or are these goals fundamentally in tension? By examining these questions in the context of predictive litigation—where efficiency imperatives meet constitutional rights and due process concerns—this study will contribute to broader conversations about the politics of automation and the possibilities for algorithmic justice.

Practical Contributions for Policy and Advocacy

This project will generate actionable insights for multiple stakeholder communities concerned with ensuring that technological innovation serves the public interest rather than entrenching inequality.

For policymakers and government administrators, this research will identify governance best practices and policy gaps across Bay Area jurisdictions. By comparing variation in oversight mechanisms, transparency requirements, and equity assessments, the study will highlight models worthy of replication as well as cautionary examples of insufficient governance. The findings can inform the development of procurement standards, algorithmic impact assessment protocols, and accountability frameworks that municipalities can adopt to ensure responsible AI deployment. Given the rapid proliferation of predictive analytics across local government functions, timely guidance grounded in empirical research is urgently needed.

For community advocates and civil rights organizations, this research will provide critical information about systems that currently operate with little public visibility. Many communities affected by municipal litigation decisions—particularly communities of color and low-income residents—have limited knowledge about how predictive technologies influence case outcomes and settlement practices. By documenting these systems and evaluating them against data justice principles, this study will equip advocates with evidence to demand greater transparency, challenge biased systems, and push for governance reforms. The research can support advocacy campaigns, inform public education efforts, and strengthen calls for algorithmic accountability.

For technology vendors and system designers, this study will illuminate the gap between how predictive litigation tools are marketed (as neutral, objective decision-support systems) and how they function in practice (as systems embedded in contested political and social contexts). Understanding the governance challenges and equity concerns that emerge in real-world deployments can inform more responsible design practices, more honest vendor claims, and more meaningful collaboration between technology providers and government clients.

Conclusion

This research has implications for the broader movement for algorithmic justice and democratic governance of technology. As AI systems become increasingly prevalent in public institutions—from education to healthcare to social services—the question of how to govern these systems democratically becomes ever more urgent. The municipal litigation context offers a particularly instructive case because it sits at the intersection of law, technology, and public accountability. Lessons learned here can inform governance approaches in other domains where algorithmic systems mediate relationships between individuals and the state.

Ultimately, this capstone project aims not only to describe how predictive litigation technologies are being deployed, but to contribute to ongoing efforts to ensure that such technologies advance rather than undermine the values of racial equity, transparency, and democratic accountability that should guide public institutions in a just society.