Machines We Trust: Perspectives on Dependable AI

Contents
List of Figures
Preface
1. Introduction
	1.1 The Machine and Us: Back to Wiener\'s Lesson
	1.2 Toward a Dependable AI?
	1.3 The Current Debate
	1.4 Structure of This Book
	Notes
	Bibliography
I. Setting the Stage
	2. Shortcuts to Artificial Intelligence
		2.1 Introduction
		2.2 Shortcuts and Debt
		2.3 Stages of a Paradigm Shift
		2.4 A New Medium
		2.5 The Recipe
		2.6 Consequences: A Rude Awakening
		2.7 Conclusions
		Acknowledgment
		Notes
		Bibliography
	3. Mapping the Stony Road toward Trustworthy AI: Expectations, Problems, Conundrums
		3.1 Introduction
		3.2 The High-Level Expert Group\'s Ethics Guidelines for Trustworthy AI
		3.3 Discussion
		3.4 Conclusion
		Notes
		Bibliography
II. Issues
	4. The Issue of Bias: The Framing Powers of Machine Learning
		4.1 Productive Bias, Wrongful Bias, and Unlawful Bias
		4.2 Machine Bias
		4.3 Ethically Problematic Bias
		4.4 The Framing Powers of ML and the Force of Law
		Notes
		Bibliography
	5. Adjudicating with Inscrutable Decision Rules
		5.1 Introduction
		5.2 Conventional Adjudication in Delegated, Distributed Decision Systems
		5.3 Explanation in Delegated, Distributed Adjudication
		5.4 Machine-Learning Models as Automated Decision Tools
		5.5 Implications of Inscrutable Automated Decision Tools for Adjudication
		5.6 Paths Forward
		Notes
		Bibliography
	6. Cobra AI: Exploring Some Unintended Consequences of Our Most Powerful Technology
		6.1 Introduction, or The AI as Part of the Automatic Infrastructure
		6.2 Paving the Road to Hell …
		6.3 Human-AI Interaction and Its Discontents
		6.4 Discussion, orWhy We Should Care
		6.5 Afterword, or Designing the Cobra Antidote
		Notes
		Bibliography
	7. The Importance of Prediction in Designing Artificial Intelligence Systems
		7.1 Introduction
		7.2 From Classical AI to Machine Learning
		7.3 Explanation and Understanding of AI Systems
		7.4 Predicting the Behavior of Autonomous Robots
		7.5 Discussion
		7.6 Conclusions
		Notes
		Bibliography
III. Prospects
	8. A Human-Centered Agenda for Intelligible Machine Learning
		8.1 The Intelligible Machine-Learning Landscape
		8.2 Who Needs Intelligibility and Why?
		8.3 Common Approaches to Intelligibility
		8.4 The Importance of Mental Models
		8.5 Beyond Model Intelligibility
		8.6 What Machine-Learning Researchers Can Learn from HCI
		8.7 Summary
		Acknowledgments
		Note
		Bibliography
	9. The AI of Ethics
		9.1 Introduction
		9.2 Seven Theses on the Ethics of AI
		9.3 Seven (Anti)Theses on the AI of Ethics
		9.4 AI Tools—Technologies of the Intellect
		Acknowledgment
		Bibliography
Contributors