Christo El Morr – författare

AI for a Just World: Power, Liberation, and the People Left Behind examines how contemporary artificial intelligence (AI) systems are reshaping social, political, and economic life and how prevailing narratives of neutrality and efficiency obscure their unequal consequences. Bringing together critical scholarship from across disciplines, this book investigates how AI redistributes power, produces new forms of exclusion, and reconfigures longstanding structures of inequality.

Across health, education, borders, labor, warfare, governance, and everyday life, contributors show how AI systems can reproduce racialized, gendered, colonial, and ableist logics while presenting these outcomes as technical objectivity. Rather than treating bias as a technical flaw to be fixed, the chapters approach AI as a socio-technical project embedded in histories of capitalism, colonial modernity, and state power, with Disability Justice as a key lens throughout this volume. Through empirical case studies, theoretical interventions, and accounts grounded in marginalized communities, this book develops concepts such as algorithmic violence, epistemic injustice, data colonialism, disability evasion, and technocolonialism. It also moves beyond critique to explore possibilities for resistance, refusal, and alternative futures, highlighting approaches rooted in relational accountability, Indigenous data sovereignty, feminist political economy, and collective care.

This volume will be of interest to scholars, graduate students, and advanced practitioners working in critical AI studies, science and technology studies, health policy, political science, sociology, education, law, and digital ethics. It will also appeal to policymakers, activists, and technologists seeking rigorous, justice-oriented frameworks for understanding and contesting the social impacts of AI.

AI for a Just World: Power, Liberation, and the People Left Behind examines how contemporary artificial intelligence (AI) systems are reshaping social, political, and economic life and how prevailing narratives of neutrality and efficiency obscure their unequal consequences. Bringing together critical scholarship from across disciplines, this book investigates how AI redistributes power, produces new forms of exclusion, and reconfigures longstanding structures of inequality.

Across health, education, borders, labor, warfare, governance, and everyday life, contributors show how AI systems can reproduce racialized, gendered, colonial, and ableist logics while presenting these outcomes as technical objectivity. Rather than treating bias as a technical flaw to be fixed, the chapters approach AI as a socio-technical project embedded in histories of capitalism, colonial modernity, and state power, with Disability Justice as a key lens throughout this volume. Through empirical case studies, theoretical interventions, and accounts grounded in marginalized communities, this book develops concepts such as algorithmic violence, epistemic injustice, data colonialism, disability evasion, and technocolonialism. It also moves beyond critique to explore possibilities for resistance, refusal, and alternative futures, highlighting approaches rooted in relational accountability, Indigenous data sovereignty, feminist political economy, and collective care.

This volume will be of interest to scholars, graduate students, and advanced practitioners working in critical AI studies, science and technology studies, health policy, political science, sociology, education, law, and digital ethics. It will also appeal to policymakers, activists, and technologists seeking rigorous, justice-oriented frameworks for understanding and contesting the social impacts of AI.

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines.

The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.