The 2020 Yearbook of the Digital Ethics Lab (Digital Ethics Lab Yearbook)

Contents
Contributors
Chapter 1: Introduction
Chapter 2: Are the Dead Taking Over Instagram? A Follow-up to Öhman & Watson (2019)
	1 Introduction
	2 Data
	3 Methodology
	4 Uncertainty
	5 Findings
	6 Discussion
	7 Conclusion
	References
Chapter 3: Emotional Self-Awareness as a Digital Literacy
	1 Introduction
	2 What Is Emotional-Self-Awareness?
	3 Social and Emotional Learning
	4 Digital Literacy
	5 Towards a More Individualized Digital Literacy
		5.1 Consuming Digital Information
		5.2 Creating Digital Information
		5.3 Learning About Digital Information
	6 Conclusion
	References
Chapter 4: The Marionette Question: What Is Yet to Be Answered about the Ethics of Online Behaviour Change?
	References
Chapter 5: On the Limits of Design: What Are the Conceptual Constraints on Designing Artificial Intelligence for Social Good?
	1 Introduction
	2 The Philosophy of Information and the Logic of Design
	3 Artificial Intelligence and Design for Social Good
	4 Collective Action Problems and the Internal Constraints on Design
	5 Cosmos, Taxis and the External Constraints on Design
	6 The Pilgrim’s Progress
		6.1 Holistic Design
		6.2 Dual System Approach
		6.3 Gradual Implementation
		6.4 Tolerant Design
		6.5 Design for Serendipity
	7 Conclusions
	References
Chapter 6: AI and Its New Winter: From Myths to Realities
	References
Chapter 7: The Governance of AI and Its Legal Context-Dependency
	1 Introduction
	2 Models of Legal Regulation
	3 A Bunch of Laws for AI
	4 Legal Context-Dependency
	5 Models of Governance for AI
	6 Conclusions
	References
Chapter 8: How to Design a Governable Digital Health Ecosystem
	1 Introduction
	2 A Systemic Approach
		2.1 Fairness at the Systems Level
		2.2 Accountability and Transparency at the Systems Level
	3 A Proactive Approach to Ethical Governance
		3.1 Data Access: Collectively Tackle the Issues of Confidentiality and Consent for the Public Good
		3.2 Data Protection: Enable Competition to Ensure Fair Return on Data Investment
		3.3 Accountability: Reframe Regulation as an Enabling Service
		3.4 Evidence: Invest in “Safe” Environments for Experimentation
	4 Keeping Society-in-the-Loop
	5 Conclusion
	References
Chapter 9: Ethical Guidelines for SARS-CoV-2 Digital Tracking and Tracing Systems
	1 The Ethical Risks of COVID-19 Digital Tracking and Tracing Systems
	2 Guidelines for Ethically Justifiable Design and Development of Digital Tracking and Tracing Systems
	3 Only One Chance to Get It Right
	References
Chapter 10: On the Risks of Trusting Artificial Intelligence: The Case of Cybersecurity
	1 Introduction
	2 Trustworthiness and Trust
	3 AI for Cybersecurity Tasks
	4 The Vulnerability of AI
	5 Making AI in Cybersecurity Reliable
	6 Conclusion
	References
Chapter 11: The Explanation Game: A Formal Framework for Interpretable Machine Learning
	1 Introduction
	2 Why Explain Algorithms?
		2.1 Justice as (Algorithmic) Fairness
		2.2 The Context of (Algorithmic) Justification
		2.3 The Context of (Algorithmic) Discovery
	3 Formal Background
		3.1 Supervised Learning
		3.2 Causal Interventionism
		3.3 Decision Theory
	4 Scope
		4.1 Complete
		4.2 Precise
		4.3 Forthcoming
	5 The Explanation Game
		5.1 Three Desiderata
			Accuracy
			Simplicity
			Relevance
		5.2 Rules of the Game
			Inputs
			Mapping the Space
			Building Models, Scoring Explanations
		5.3 Consistency and Convergence
	6 Discussion
	7 Objections
		7.1 Too Highly Idealised
		7.2 Infinite Regress
		7.3 Pragmatism + Pluralism = Relativist Anarchy?
		7.4 No Trade-off
		7.5 Double Standards
	8 Conclusion
	References
Chapter 12: Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-offs
	1 Introduction
	2 Discrimination in Mortgage Lending
		2.1 Legal Framework for Discrimination
	3 Sources of Discriminatory Bias
		3.1 Over-Estimation of Minority Risk
		3.2 Under-Estimation of Minority Risk
	4 Impact of Algorithms
	5 Methodology
		5.1 Data
		5.2 Algorithms
	6 Limitations of Existing Fairness Literature
		6.1 Ex Post Fairness
		6.2 Group Fairness
		6.3 Equalisation of Evaluation Metrics
		6.4 Fairness Impossibility
		6.5 Proxies of Race and Proxies of Risk
		6.6 Existing Structural Bias
	7 Ex Ante Fairness
		7.1 Individual Fairness
		7.2 Counterfactual Fairness
	8 Limitations in Existing Approaches to Fairness
	9 Proposal of Trade-off Analysis
		9.1 Operationalisation of Variables
		9.2 Financial Inclusion
		9.3 Negative Impact on Minorities
	10 Trade-off Analysis
		10.1 Proxies of Race
		10.2 Triangulation of Applicant’s Race
	11 Limitations and Future Work
	12 Conclusion
	Appendix
		Features
	References
Chapter 13: Ethical Foresight Analysis: What It Is and Why It Is Needed?
	1 Introduction
	2 Background
		2.1 Definitions
		2.2 A Brief History of Foresight Analysis
		2.3 Relevant Concepts for Ethical Foresight Analysis
		2.4 When is Ethical Foresight Analysis Useful?
	3 Existing Methodologies of Ethical Foresight Analysis
		3.1 Crowdsourced Single Predictions Frameworks (Delphi and Prediction Markets)
		3.2 Evaluation
		3.3 Technology Assessment (TA)
		3.4 Evaluation
		3.5 Debate-Oriented Frameworks (eTA)
		3.6 Evaluation
		3.7 Far Future Techniques (Techno-Ethical Scenarios Approach, TES)
		3.8 Evaluation
		3.9 Government and Policy Planning Techniques (ETICA)
		3.10 Evaluation
		3.11 Combinatory Techniques (Anticipatory Technology Ethics, ATE)
		3.12 Evaluation
	4 Discussion: Known Limitations of EFA
	5 Recommendations for Potential Future Approaches to EFA
	6 Conclusion
	References
Chapter 14: Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions
	1 Introduction
		1.1 What Are the Fundamentally Unique and Plausible Threats Posed by AIC?
		1.2 What Solutions Are Available or May be Devised to Deal with AIC?
	2 Methodology
	3 Threats
		3.1 Commerce, Financial Markets, and Insolvency
	4 Harmful or Dangerous Drugs
	5 Offences against the Person
	6 Sexual Offences
	7 Theft and Fraud, and Forgery and Personation
	8 Possible Solutions for Artificial Intelligence-Supported Crime
		8.1 Tackling Emergence
		8.2 Addressing Liability
		8.3 Monitoring
		8.4 Psychology
	9 Conclusions
		9.1 Areas
		9.2 Dual-Use
		9.3 Security
		9.4 Persons
		9.5 Organisation
	References