We are entering a new era of technological determinism and solutionism in which governments and business actors are seeking data-driven change, assuming that Artificial Intelligence is now inevitable and ubiquitous. But we have not even started asking the right questions, let alone developed an understanding of the consequences. Urgently needed is debate that asks and answers fundamental questions about power. This book brings together critical interrogations of what constitutes AI, its impact and its inequalities in order to offer an analysis of what it means for AI to deliver benefits for everyone.
The book is structured in three parts: Part 1, AI: Humans vs. Machines, presents critical perspectives on human-machine dualism. Part 2, Discourses and Myths About AI, excavates metaphors and policies to ask normative questions about what is 'desirable' AI and what conditions make this possible. Part 3, AI Power and Inequalities, discusses how the implementation of AI creates important challenges that urgently need to be addressed. Bringing together scholars from diverse disciplinary backgrounds and regional contexts, this book offers a vital intervention on one of the most hyped concepts of our times.
This open book is licensed under a Creative Commons License (CC BY-NC-ND). You can download AI for Everyone? ebook for free in PDF format (17.2 MB).
Table of Contents
Introduction: Why We Need Critical Perspectives on AI
Artificial Intelligence (AI): When Humans and Machines Might Have to Coexist
Digital Humanism: Epistemological, Ontological and Praxiological Foundations
An Alternative Rationalisation of Creative AI by De-Familiarising Creativity: Towards an Intelligibility of Its Own Terms
Post-Humanism, Mutual Aid
The Language Labyrinth: Constructive Critique on the Terminology Used in the AI Discourse
AI Ethics Needs Good Data
The Social Reconfiguration of Artificial Intelligence: Utility and Feasibility
Creating the Technological Saviour: Discourses on AI in Europe and the Legitimation of Super Capitalism
AI Bugs and Failures: How and Why to Render AI-Algorithms More Human?
Primed Prediction: A Critical Examination of the Consequences of Exclusion of the Ontological Now in AI Protocol
Algorithmic Logic in Digital Capitalism
'Not Ready for Prime Time': Biometrics and Biopolitics in the (Un)Making of California's Facial Recognition Ban
Beyond Mechanical Turk: The Work of Brazilians on Global AI Platforms
Towards Data Justice Unionism? A Labour Perspective on AI Governance