Christo El Morr – författare

AI''s impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

AI''s impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

AI's impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

AI's impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

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.

The first resource of its kind, Introduction to Health Informatics examined the effects of health informatics on healthcare practitioners, patients, and policies from a distinctly Canadian perspective. This second edition has been thoroughly updated to reflect current trends and innovations in health informatics, and includes new figures, charts, tables, and web links.In this text, author Christo El Morr presents the subject of health informatics in an accessible, concise way, breaking the topic down into 12 chapters divided into 3 sections. Each chapter includes objectives, key terms, which are defined in a full glossary at the end of the text, and a "Test Your Understanding" section for student review. The second edition also features 15% brand new content, with a full chapter on analytics, machine learning, and AI for health, as well as information on virtual care, mHealth apps, COVID-19 responses, adoption of EHR across provinces, clinical informatics, and precisions medicine.Packed with pedagogical features and updated instructor supplements, this text is a vital resource for students, instructors, and practitioners in health informatics, health management, and health policy.

This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.

This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.

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.

This book offers a bold and critical examination of the intersections between artificial intelligence (AI) and disability justice. Rather than framing AI as a neutral or purely beneficial tool, the book interrogates how AI systems reproduce ableism, deepen systemic exclusions, and perpetuate colonial, neoliberal, and corporate power structures. It challenges the prevailing reliance on "tech fixes"—improving datasets or tweaking algorithms—and instead insists on addressing the deeper social, political, and ethical dimensions of AI development and deployment. Through a disability justice lens, it highlights the complex realities faced by disabled communities and emphasizes the need for approaches that dismantle ableism rather than superficially enhance accessibility.Covering topics such as algorithmic bias, data colonialism, technoableism, and the militarization of AI, this book foregrounds critical frameworks including design justice, data sovereignty, and participatory governance. It draws on diverse scholarly perspectives and activist insights to propose actionable pathways toward more accountable, equitable, and inclusive AI futures. By centering disabled people’s experiences and knowledge, the book offers not only critique but also visionary strategies for transforming AI systems to serve justice rather than entrench oppression.

This book offers a bold and critical examination of the intersections between artificial intelligence (AI) and disability justice. Rather than framing AI as a neutral or purely beneficial tool, the book interrogates how AI systems reproduce ableism, deepen systemic exclusions, and perpetuate colonial, neoliberal, and corporate power structures. It challenges the prevailing reliance on "e;tech fixes"e;—improving datasets or tweaking algorithms—and instead insists on addressing the deeper social, political, and ethical dimensions of AI development and deployment. Through a disability justice lens, it highlights the complex realities faced by disabled communities and emphasizes the need for approaches that dismantle ableism rather than superficially enhance accessibility.Covering topics such as algorithmic bias, data colonialism, technoableism, and the militarization of AI, this book foregrounds critical frameworks including design justice, data sovereignty, and participatory governance. It draws on diverse scholarly perspectives and activist insights to propose actionable pathways toward more accountable, equitable, and inclusive AI futures. By centering disabled people’s experiences and knowledge, the book offers not only critique but also visionary strategies for transforming AI systems to serve justice rather than entrench oppression.