08298nam 2200517 450 991050845080332120231110221708.03-030-80083-0(CKB)5470000001298817(MiAaPQ)EBC6796389(Au-PeEL)EBL6796389(OCoLC)1283859197(EXLCZ)99547000000129881720220721d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierThe 2021 yearbook of the digital ethics lab /edited by Josh Cowls, Jessica MorleyCham, Switzerland :Springer,[2021]©20211 online resource (230 pages)Digital Ethics Lab Yearbook 3-030-80082-2 Intro -- Contents -- Contributors -- Chapter 1: Introduction -- Chapter 2: Are the Dead Taking Over Instagram? A Follow-up to Öhman &amp -- 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.Digital Ethics Lab Yearbook Computer scienceMoral and ethical aspectsPeriodicalsInformation technologyMoral and ethical aspectsPeriodicalsArtificial intelligenceMoral and ethical aspectsPeriodicalsComputer scienceMoral and ethical aspectsInformation technologyMoral and ethical aspectsArtificial intelligenceMoral and ethical aspects303.4Cowls JoshMorley JessicaMiAaPQMiAaPQMiAaPQBOOK9910508450803321The 2021 Yearbook of the Digital Ethics Lab2967894UNINA