Eubanks investigates how automation in public service delivery—such as welfare, housing, and criminal justice—perpetuates bias and harm. This book directly connects to the project’s examination of algorithmic fairness and community accountability. 

O’Neil exposes how predictive models and algorithms can amplify social inequality when used without transparency or accountability. Her critique lays the groundwork for understanding the risks of predictive litigation tools in government systems. 

Green argues that cities should prioritize democratic governance and equity over technological efficiency. His work helps frame how municipal governments can responsibly adopt predictive systems without sacrificing public trust. 

Crawford examines AI’s social, political, and environmental impacts, encouraging readers to see beyond algorithms to the global systems that sustain them. This lens is essential for analyzing data justice in local government contexts. 

Pasquale critically examines opaque algorithms in governance and finance, offering insight into accountability and transparency concerns.

Greenfield critiques "smart city" initiatives, offering a critical lens on municipal algorithmic governance.

The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. AI is becoming more and more a part of everyone’s life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book’s many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.

But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.

When AI is framed as cheap prediction, its extraordinary potential becomes clear:

• Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions.

• Prediction tools increase productivity--operating machines, handling documents, communicating with customers.

• Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete.
  Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

AI is poised to disrupt our work and our lives. We can harness these technologies rather than fall captive to them―but only through wise regulation.

Too many CEOs tell a simple story about the future of work: if a machine can do what you do, your job will be automated. They envision everyone from doctors to soldiers rendered superfluous by ever-more-powerful AI. They offer stark alternatives: make robots or be replaced by them.

Another story is possible. In virtually every walk of life, robotic systems can make labor more valuable, not less. Frank Pasquale tells the story of nurses, teachers, designers, and others who partner with technologists, rather than meekly serving as data sources for their computerized replacements. This cooperation reveals the kind of technological advance that could bring us all better health care, education, and more, while maintaining meaningful work. These partnerships also show how law and regulation can promote prosperity for all, rather than a zero-sum race of humans against machines.

How far should AI be entrusted to assume tasks once performed by humans? What is gained and lost when it does? What is the optimal mix of robotic and human interaction? New Laws of Robotics makes the case that policymakers must not allow corporations or engineers to answer these questions alone. The kind of automation we get―and who benefits from it―will depend on myriad small decisions about how to develop AI. Pasquale proposes ways to democratize that decision making, rather than centralize it in unaccountable firms. Sober yet optimistic, New Laws of Robotics offers an inspiring vision of technological progress, in which human capacities and expertise are the irreplaceable center of an inclusive economy. 

Benjamin explores how algorithms reproduce racial hierarchies and calls for abolitionist approaches to technology governance—key to understanding racial equity in predictive analytics. 

D’Ignazio and Klein introduce feminist data principles emphasizing intersectionality, transparency, and power redistribution—offering practical frameworks for ethical data governance. 

Kearns and Roth explore how algorithms can be designed to balance accuracy, privacy, and fairness—relevant to implementing predictive models in public administration. 

This book critiques how bias gets embedded in everyday tech design, encouraging municipal actors to prioritize inclusion and ethics in digital services

Mossberger and colleagues examine how digital divides affect governance and participation in U.S. cities, providing insight into how equitable technology adoption can enhance civic inclusion 

Townsend traces the rise of the “smart city” movement, offering both promise and caution for municipalities deploying AI-driven systems. 

This book specifically explores the intersection of data‑driven urbanism and social justice, addressing how “data justice” is implicated in urban governance. It looks at issues of citizenship, rights, community, and the datafied city. 

Kalpokas gives you a theoretical anchor for how such tools alter power, accountability, regulation — and you can apply it to municipal governments. 

Clark critiques how “smart city” initiatives, though often promoted as inclusive, can exacerbate social and spatial inequalities. 

This book explicitly links algorithms, public administration, urban governance, and criminal justice -- making it highly relevant for the theme of predictive litigation in municipal government. 

This more technical book addresses predictive analytics and how human judgment, bias, and methods are incorporated (or neglected) in predictive systems.