Multidisciplinary Perspectives On Artificial Intelligence And The Law

Preface
About the Book
Acknowledgments
Contents
Editors and Contributors
About the Editors
Contributors
Part I Scientific, Technological and Societal Achievements in Artificial Intelligence
Introduction
Artificial Intelligence: Historical Context and State of the Art
1 Historical Origins
2 Can Machines Think?
3 Objections to Artificial Intelligence
4 Intelligence as Symbol Manipulation
5 Machine Learning
5.1 Basic Concepts
5.2 Statistical Approaches
5.3 Similarity-Based Approaches
5.4 Decision Trees
5.5 Neural Networks
6 The Deep Learning Revolution
7 Applications in Analytics and Automation
8 Conclusions
References
The Impact of Language Technologies in the Legal Domain
1 Introduction
2 Language Processing Technologies for Processing Textual Data
2.1 Text Anonymization
2.2 Document Classification
2.3 Information Retrieval
2.4 Information Extraction
2.5 Summarization
2.6 Question Answering and Conversational Systems
2.7 Predictions Supported on Textual Evidence
2.8 Summary
3 Spoken Language Technologies
3.1 Automatic Speech Recognition
3.2 Speaker Recognition and Speaker Profiling
3.3 Speech Synthesis and Voice Conversion
4 Conclusions
References
Societal Implications of Recommendation Systems: A Technical Perspective
1 Introduction
2 Recommendation Systems
3 When Recommendation Systems Work
3.1 Implications for Consumption
3.2 Implications for Democracy
4 When Recommendation Systems Fail
4.1 Learning from Biased Data: Implications for Individuals
4.2 From Bad Algorithms to Discriminatory Policies
5 A Way Forward
6 Conclusions
References
Data-Driven Approaches in Healthcare: Challenges and Emerging Trends
1 Patient-Centered Care, Value-Based Care and the P4 Medicine Paradigm: Divergent or Complementary?
2 Data-Driven Healthcare
3 Ethics and Legal Challenges Posed by Artificial Intelligence
4 Investments Trends in Healthcare Artificial Intelligence
References
Security and Privacy
1 Introduction
2 Defining Security and Privacy
2.1 Security Properties
2.2 Privacy Properties
3 Security and Privacy Problems
3.1 Access Control
3.2 Vulnerabilities and Attacks
3.3 Malware
3.4 The Human Factor
4 Scientific and Technological Achievements
4.1 Cryptography
4.2 Hardware-Based Security
4.3 Cloud Computing
4.4 Digital Money, Assets and Identity
5 Security, Privacy, and Machine Learning
6 Censorship Resistance
6.1 Anonymity Networks
6.2 Multimedia Protocol Tunneling
6.3 Avoiding ML Attacks
7 Conclusion
References
Part II Ethical and Legal Challenges in Artificial Intelligence
Introduction
Before and Beyond Artificial Intelligence: Opportunitiesand Challenges
1 Few Presuppositions that Shape the Reflection on AI
2 Can Machines Imitate Humans?
2.1 The Key Question
2.2 The First AI Steps
2.3 The Encouraging Achievements
3 Can Humans Imitate Machines?
3.1 Functional Level
3.2 Structural Level
3.3 Identity Level
4 How Should (Ethics)/Ought (Law) Humans and Machines Relate?
4.1 Ethical Requirements
4.2 Law and Legal Procedures
5 Concluding Remarks
References
Autonomous and Intelligent Robots: Social, Legal and Ethical Issues
1 Introduction
2 Industrial Robots and Automation vs Service Robots
3 Robots and Humans: The Rise of Intelligent and Social Robots
4 Ethical, Social and Legal Impacts
4.1 Ethical Issues
4.2 Social Issues
4.3 Legal Issues
5 Conclusions
References
The Ethical and Legal Challenges of Recommender Systems Driven by Artificial Intelligence
1 Introduction
2 What are AI's Recommender Systems?
3 Ethical and Legal Challenges Associated with RS
3.1 Opacity
3.2 Discriminatory Bias
3.3 Privacy and Data Protection Violations
3.4 Diminished Human Autonomy and Self-Determination
3.5 Polarization and Manipulation of Democratic Processes
4 Recommender Systems: Legal and Regulatory Challenges
4.1 Lack of Transparency
4.2 Trade Secret
4.3 Constantly Changing Technology
4.4 Difficulties of Implementation of Data Subjects' Rights in Practice
4.5 Difficulties of Rules' Application
4.6 Beyond Damage Prevention
5 Strategies and Possible Solutions to the Challenges Created by RS
5.1 Best Practices Beyond Law
5.1.1 Regulation by Technology: Strategies by Design and by Default
5.1.2 Implementation of (Human Rights) Impact Assessments
5.1.3 Guarantee of Greater Transparency and Explanation of AI (Explainable AI)
5.1.4 Codes of Conduct (Self-Regulation)
5.1.5 Digital Education in AI
5.2 Specific Legal Regulation for AI Systems
5.2.1 Digital Services Act (DSA)
5.2.2 Proposal of an Artificial Intelligence Act (AIA)
6 Conclusion
References
Metacognition, Accountability and Legal Personhood of AI
1 Introduction
2 What Is the Common Denominator in Agency?
3 What Is a Voluntary Act?
4 What Makes an Agent a Legally Responsible One?
5 Metacognition: Shaping Legal Responsibility
6 Accountability and Legal Personhood
7 Conclusions
References
Artificial Intelligence and Decision Making in Health: Risks and Opportunities
1 Introduction
2 Decision-Making Processes in Health and AI
2.1 The Health Area the Use of AI and Decision-Making Processes: Opportunities and Risks to Treat Electronic Health Records (EHR)
2.2 The Opportunities
2.3 The Risks
3 Complex Bioethics Model (CBM) and AI
4 Conclusion
References
The Autonomous AI Physician: Medical Ethics and Legal Liability
1 Introduction
2 Artificial Intelligence in Pathology
3 The Autonomous AI Physician: Parameters
4 Ethical and Legal Implications of the Autonomous AI Physician
4.1 Ethical Consideration: Transparency
4.2 Ethical Considerations: Reliability and Safety
4.3 Ethical Consideration: Bias
4.4 Legal Considerations: Data Privacy
4.5 Legal Consideration: Liability
5 Regulating the Autonomous AI Physician
5.1 Healthcare Industry Regulation
5.2 Government Regulation
5.2.1 Safety Regulation
5.2.2 Data Regulation
5.3 Liability for Injuries
5.3.1 Products Liability
5.3.2 Organizational, Vicarious, and Enterprise Liability
5.3.3 Medical Malpractice
5.3.4 Contractual Assignment of Liability
5.3.5 Special Adjudication Systems
6 Conclusion
References
Ethical Challenges of Artificial Intelligence in Medicine and the Triple Semantic Dimensions of Algorithmic Opacity with Its Repercussions to Patient Consent and Medical Liability
1 Introduction: Advantages of Artificial Intelligence (AI) in Medicine
2 Triple Semantic Dimensions of Algorithmic Opacity and Its Repercussions to Patient Consent and Medical Liability
3 Ethical Dimensions of Using Artificial Intelligence (AI) in the Healthcare Sector: Setting the Parameters for Data-Informed Duties in Tort Law
4 Concluding Notes: The Future of Artificial Intelligence (AI) in Medicine and the Importance of Medical Education in Digital Health and New Technologies
References
Part III The Law, Governance and Regulation of Artificial Intelligence
Introduction
Dismantling Four Myths in AI & EU Law Through Legal Information `About' Reality
1 Introduction
2 Digital Sovereignty
3 Digital Constitutionalism
4 The Brussels Effect
5 `HAI' (Human-Centric Artificial Intelligence)
6 Conclusions
References
AI Modelling of Counterfactual Thinking for Judicial Reasoning and Governance of Law
1 Introduction and Motivation
2 Some Societal and Historical Background
3 On Counterfactual Reasoning
4 Counterfactual Reasoning and Conflicts of Interest in Large Populations
5 Stag Hunting and Law: From Plea Bargaining to International Agreements and AI Regulation
6 Evolutionary Games with Counterfactual Thinking (CT)
7 Concluding Remarks
References
Judicial Decision-Making in the Age of Artificial Intelligence
1 Introduction
2 The Sentencing Process
3 S v Loomis
4 The “Technology Effect”
5 “Automation Bias” and the Anchoring Effect
6 Conclusion
References
Liability for AI Driven Systems
1 Presentation of the Problems
2 Subjective Liability in Case of Alternative Causation
3 Strict Liability
4 Exemption from Liability for Damage Caused by an AI System
References
Risks Associated with the Use of Natural Language Generation: Swiss Civil Liability Law Perspective
1 Technical Basics on Natural Language Generation
1.1 Introduction to Technical Aspects
1.2 Risks of Reinforcement Learning
1.2.1 Undesirable Language Generation
1.2.2 Code Generation and Vulnerable Code Data
1.3 Detection of Machine Generated Text
1.4 Operator Influence on Output
1.4.1 General Remarks
1.4.2 Data and Methods
1.4.3 Samples of Operator Influence
2 Legal Aspects
2.1 Introduction to Legal Analysis
2.2 Liability for Autonomous Actions of AI in General
2.2.1 Unforeseeable Actions of Self-Learning AI as a Challenge for Tort Law
2.2.2 Respondent to Tort Claim
2.2.3 Causality as the Limiting Factor of Liability
2.3 Directive on Defective Products
2.3.1 General Remarks
2.3.2 Defectiveness of an AI System
2.3.3 State of the Art Defense
2.4 Liability for Negligence
2.4.1 Infringement of Intellectual Property Rights
2.4.2 Personal Rights Violation
2.4.3 Unfair Competition
2.4.4 Duty of Care
3 Conclusion
References
AI Instruments for Risk of Recidivism Prediction and the Possibility of Criminal Adjudication Deprived of Personal Moral Recognition Standards: Sparse Notesfrom a Layman
1 Introduction
2 The Predictability of Future Behavior
2.1 The Acceptance of a Judgment of Probability as a Criterion and Basis for Limiting Physical Freedom: Its Implications and Its Consequences
3 The Risk of Technological Bias
4 Conclusion
References
The Relevance of Deepfakes in the Administration of Criminal Justice
1 Introduction
2 Deepfake: Definition and Categories
3 AI and Deepfake
4 Machine Learning
4.1 Supervised Learning
4.2 Unsupervised Learning
4.3 Semi-Supervised Learning
4.4 Reinforcement Learning
5 Deep Learning
6 Deepfake Generation
7 Deepfake Detection
7.1 Image Detection Models
7.2 Video Detection Models
8 Deepfakes and the Administration of Criminal Justice
9 Conclusion
References
Antitrust Law and Coordination Through Al-Based PricingTechnologies
1 Introduction
2 Algorithmic Pricing and Collusion
3 Varieties of AI-Based Pricing Technologies
4 Algorithmic Collusion in US Antitrust Law
4.1 The Necessity of “Agreement”
4.2 Non-Agreement Coordination
4.3 Hub-and-Spoke
4.4 Remedying Coordinated Conduct
5 Algorithmic Collusion in EU Antitrust Law
5.1 Coordination by Agreement
5.2 Non-Agreement Coordination
5.3 Hub-and-Spoke Coordination
5.4 Collective Dominance
6 Comparative Legal Analysis of Algorithmic Pricing Situations
7 Voices of Policy Makers and Future Outlook
8 Conclusion
References
The “Artificial Intelligence Act” Proposal on European e-Justice Domains Through the Lens of User-Focused, User-Friendly and Effective Judicial Protection Principles
1 E-Justice Paradigm and the Trend of Digitalization of Justice: The Time Is Now for Tackling Artificial Intelligence Pros and Cons
2 Artificial Intelligence Systems Intended to the Administration of Justice
2.1 Artificial Intelligence Systems on Organising Information
2.2 Artificial Intelligence Systems on Mobilising Useful Pre-Existing Case-Law
2.3 Artificial Intelligence Systems on Forecasting Decision Trends (Predictive Justice)
3 High-Risk Classification of Artificial Intelligence Systems: Human-Centric Approach at the Service of Effective Judicial Protection Domains
4 Conclusive Remarks
References
The European Union's Approach to Artificial Intelligence and the Challenge of Financial Systemic Risk
1 Introduction
2 AI Uses in Finance and Systemic Risk
2.1 The Opportunities and Risks of AI FinTech
2.2 The Impact of AI on the Cross-Sectional and Time Dimensions of Systemic Risk
3 The EU's Approach to AI and the Challenge of Systemic Risk
3.1 One Approach, Two Pillars
3.2 The EU's Approach to AI
3.3 Missing the Opportunity to Regulate the Systemic Risk Amplified by AI
4 Conclusion
References
Regulating AI: Challenges and the Way Forward Through Regulatory Sandboxes
1 Introduction
2 The “Taming” of AI by the Law
3 Playing in the Sand
4 The Way Forward Through the AI Act
5 Conclusion
References