Law And Artificial Intelligence: Regulating AI And Applying AI In Legal Practice

Acknowledgements
Contents
About the Editors
Part I Introduction
1 Humanizing Machines: Introduction and Overview
1.1 The Rise of Artificial Intelligence
1.2 So What is New About This?
1.2.1 A New Technology
1.2.2 A New Need for Regulation
1.2.3 A New Book
1.3 What This Book is About
1.3.1 The Novelty of This Book
1.3.2 Readership and Target Audience
1.4 Leiden University and the SAILS Project
1.5 The Structure of This Book
1.5.1 Part I: Introduction
1.5.2 Part II: Public Law
1.5.3 Part III: Private Law
1.5.4 Part IV: Legal Practice
1.5.5 Part V: The Future of AI
References
2 Artificial Intelligence Versus Biological Intelligence: A Historical Overview
2.1 Introduction
2.2 The Beginnings of Artificial Intelligence and Cognitive Science
2.3 Symbolic AI and Physical Symbol Systems
2.4 Artificial Neural Networks
2.5 Conclusion
References
3 Disciplines of AI: An Overview of Approaches and Techniques
3.1 Introduction
3.2 Definitions of AI
3.3 Disciplines of AI
3.3.1 Machine Learning
3.3.2 Natural Language Processing
3.3.3 Computer Vision and Face Recognition
3.3.4 Affective Computing
3.3.5 Automated Reasoning
3.4 Conclusion
References
Part II Public Law
4 Discrimination by Machine-Based Decisions: Inputs and Limits of Anti-discrimination Law
4.1 Introduction
4.2 Indirect Discrimination
4.3 Addressing Algorithmic Discrimination Through Indirect Discrimination: Potential Shortcomings
4.4 Conclusions
References
5 Women's Rights Under AI Regulation: Fighting AI Gender Bias Through a Feminist and Intersectional Approach
5.1 Introduction
5.2 Gender Biases in AI Development
5.2.1 The Challenges of Missing Sex and Gender Considerations in Algorithms
5.2.2 Scholarly Approaches to Mitigate AI Gender Biases
5.3 Increased Attention to Diversity and Inclusion in the Development of AI from the Regulatory Perspective
5.3.1 UNESCO Recommendation on Guiding Principles for AI
5.3.2 Toronto Declaration
5.3.3 European Approach to AI Regulation
5.4 The Promise of AI through Diversity and Inclusion Lenses
5.5 Reflections and Discussion
5.6 Conclusions
References
6 Diversity and Inclusion in Artificial Intelligence
6.1 Introduction
6.2 Diversity and Inclusion in Artificial Intelligence
6.2.1 Technical Level
6.2.2 Community Level
6.2.3 Target User Level
6.3 Implications of Missing Diversity and Inclusion in AI
6.3.1 Gendered Social Robots: The Mechanization of Women
6.3.2 Binary Gender Classifiers: Guessing Objectively What is Subjective
6.3.3 Algorithms for Medical Applications: Gender as a Safety Parameter
6.3.4 Sex Robotics: Able-Bodied and Male-Dominated Markets
6.4 Addressing Diversity and Inclusion in AI
6.4.1 Diversity in Algorithms: Gendering Algorithms
6.4.2 Diverse Teams, Organizations, and Design
6.4.3 More Inclusive Guidelines, Policies, and Regulation
6.5 Conclusion
References
7 Artificial Intelligence in Disability Employment: Incorporating a Human Rights Approach
7.1 Introduction
7.2 AI Technologies: Towards Bridging the Gap Between Disability and the Labour Market
7.3 Applicable Human Rights Standards
7.4 Conclusions
References
8 Prosecuting Killer Robots: Allocating Criminal Responsibilities for Grave Breaches of International Humanitarian Law Committed by Lethal Autonomous Weapon Systems
8.1 Delineating the Accountability Problem
8.2 Lethal Autonomous Weapon Systems
8.3 Criminal Responsibilities
8.3.1 Individual Criminal Responsibility
8.3.2 Commander Responsibility
8.4 Conclusion
References
9 The Risks of Social Media Platforms for Democracy: A Call for a New Regulation
9.1 Introduction
9.2 A Destabilising Technological Change: Big Data and Algorithms
9.3 The Datafication
9.3.1 The Economic Incentive
9.3.2 Attention and Distraction
9.4 Filter Bubbles, Polarisation and Misinformation
9.5 Approaches to the Regulation of Social Media
9.6 Conclusions
References
10 Biased Algorithms and the Discrimination upon Immigration Policy
10.1 Introduction
10.2 Migrants as Subjects of Human Rights
10.3 International Commitments to End the Discrimination that Applies to Migratory Policies
10.4 Application of Algorithms in the Decision-Making Process of Migratory Policies
10.4.1 United Kingdom—Granting Visas
10.4.2 EU—Arrival
10.4.3 New Zealand—(Over) Staying
10.4.4 Canada—Entry Application
10.4.5 Canada—Deportation
10.5 The Incorporation of Biases in Algorithms
10.6 Consequences of Biased/Wrong Decisions
10.7 Conclusion
References
11 AI in Criminal Law: An Overview of AI Applications in Substantive and Procedural Criminal Law
11.1 Introduction
11.2 AI and Substantive Criminal Law
11.2.1 Crimes
11.2.2 Sanctions and Justice-Related Programmes
11.2.3 Legal Questions
11.3 AI and Procedural Criminal Law
11.3.1 Criminal Investigation
11.3.2 Evidence
11.3.3 Legal Questions
11.4 Conclusions
References
12 Black-Box Models as a Tool to Fight VAT Fraud
12.1 Introduction
12.2 Artificial Intelligence: Brief Introduction
12.3 STIR: A Tool to Detect VAT Fraud in Poland
12.4 Data Protection Legislation
12.5 Fundamental Human Rights
12.6 Conclusions
References
Part III Private Law
13 Bridging the Liability Gaps: Why AI Challenges the Existing Rules on Liability and How to Design Human-empowering Solutions
13.1 Introduction
13.2 Mind the Gap: The Disruption to the Liability Rules Caused by AI
13.3 State of the Art: Proposed Legal Solutions to the Liability Gaps
13.3.1 Contractual and Extra-contractual Liability (Fault or Negligence)
13.3.2 Scenario A: Alice and The Bank
13.3.3 Another Form of Extra-contractual Liability: Strict Liability
13.3.4 Scenario B: Alice and Autonomous Vehicles
13.3.5 Scenario C: Alice and the Smart Home
13.3.6 Beyond Liability: Other Legal Tools
13.4 Reflections: Putting Humans Back at the Centre
13.5 Conclusions
References
14 Contractual Liability for the Use of AI under Dutch Law and EU Legislative Proposals
14.1 Introduction
14.2 Breach of Contract
14.3 Attribution
14.3.1 Fault
14.3.2 The Law
14.4 AI and Property Law
14.4.1 The Concept of Object in the Dutch Civil Code
14.4.2 The Doctrine of Functional Equivalence
14.4.3 Application of the Functional Equivalence Doctrine
14.5 Conclusion
References
15 Digging into the Accountability Gap: Operator’s Civil Liability in Healthcare AI-systems
15.1 Introduction
15.2 The Proposal
15.3 The Doctrinal Instability
15.3.1 The Product/Service Dichotomy
15.3.2 The Causation Turbulence
15.4 Economic Analysis
15.4.1 The Two Basic Functions of Liability Law
15.4.2 A Change in Context Factors
15.4.3 The Expected Effect of the Liability Regime
15.5 Conclusion and Further Research
References
16 Automated Care-Taking and the Constitutional Rights of the Patient in an Aging Population
16.1 Introduction
16.2 The Role and Functions of Social Robots in the Care of the Elderly
16.2.1 Robots for Assisting the Elderly
16.2.2 Robots for Monitoring and Supervision
16.2.3 Robots for Companionship
16.3 The Right to Care in its Moral and Legal Dimensions
16.3.1 Comparing the Functioning of Different Welfare Systems in Europe
16.3.2 Technology-Based Care Services, and the Problem of Human-Machine Interaction
16.4 Ethical Frameworks for the Assessing the Impact on the Rights of Users and Patients
16.4.1 A Utilitarian Approach
16.4.2 A Capability Approach
16.4.3 A Deontological Perspective
16.4.4 Dignity as an Ethical and Legal Concept
16.5 Conclusion
References
17 Generative AI and Intellectual Property Rights
17.1 Introduction
17.2 AI: The Generative Form
17.2.1 Main Approaches
17.2.2 Walk in the Park
17.3 AI-Generated Works in IPR
17.4 Constructing Authorship Rights for AI-Generated Works
17.4.1 Made for Hire
17.4.2 The Attribution of Legal Personhood to AI-Systems
17.4.3 Computer-Generated Works
17.4.4 Sui Generis Rights
17.4.5 Originality: The Elephant in the Room
17.4.6 Related Rights
17.5 Should AI-Generated Works Be Protected?
17.5.1 The Utilitarian Argument for Protection
17.5.2 Stretching the Original Myth
17.5.3 Carving Out Spaces
17.6 Conclusion
References
18 The Role and Legal Implications of Autonomy in AI-Driven Boardrooms
18.1 Introduction
18.2 Integrating Artificial Intelligence into the BoD
18.2.1 Defining the BoD Anatomy and Functioning
18.2.2 The Applications and Capabilities of AI on the BoD
18.3 Autonomy Levels of AI in the Boardroom Context
18.4 Discussion
18.5 Conclusion
References
19 Artificial Intelligence and European Competition Law: Identifying Principles for a Fair Market
19.1 Background and Structure
19.2 Basic Concepts of Competition Law
19.2.1 Basics for All Companies
19.2.2 Basics for Dominant Companies
19.2.3 Fair Competition
19.3 The Use of Algorithms
19.3.1 Relations and Interaction of Algorithms
19.3.2 Position of Users of Algorithms
19.3.3 Evaluation
19.4 Data
19.4.1 Cooperation on Data
19.4.2 Position of Owners of Data
19.4.3 Evaluation
19.5 A Fair Market
19.6 Conclusion
References
20 Personalised Shopping and Algorithmic Pricing: How EU Competition Law Can Protect Consumers in the Digital World
20.1 Background and Structure of the Chapter
20.2 Conceptualising Online Price Discrimination
20.3 Welfare Analysis of Personalised Pricing
20.4 Fairness Considerations of Personalised Pricing
20.5 Application of Abuse of Dominance Provisions to Personalised Pricing
20.6 Price Discrimination under Article 102(C) TFEU
20.7 Conclusion
References
Part IV Legal Practice
21 Lawyers’ Perceptions on the Use of AI
21.1 Introduction
21.2 Review of the Substantive Literature
21.2.1 Defining Legal Tech, AI and ML
21.2.2 Sustainable Competitive Advantage
21.3 Meta-Synthesis Analysis
21.3.1 Meta-Synthesis and Interpretative Research
21.3.2 Searching for Relevant Surveys
21.3.3 Evaluating the Quality of Surveys
21.3.4 Analysis and Integration of Survey Outcomes
21.3.5 Findings from the Meta-Synthesis
21.4 Expert Sampling
21.5 Conclusions and Further Research
References
22 AI and Lawmaking: An Overview
22.1 Introduction
22.2 Legislation and AI
22.2.1 Introducing Legislation as an AI Topic
22.2.2 Introducing the DSO
22.3 AI and Legislative Technique
22.3.1 Alignment
22.3.2 Challenges
22.4 AI and Legislative Process
22.4.1 Alignment
22.4.2 Challenges
22.5 AI and Legislative Monitoring
22.5.1 Alignment
22.5.2 Challenges
22.6 Concluding Remarks
References
23 Ask the Data: A Machine Learning Analysis of the Legal Scholarship on Artificial Intelligence
23.1 Introduction
23.2 Evaluating Legal Scholars’ Interest in Artificial Intelligence
23.3 Automating Text Analysis in Legal Research: The Potential of Topic Modeling
23.4 Data and Methods
23.4.1 Data
23.4.2 Methods
23.5 Results
23.5.1 Descriptive Details of the Dataset
23.5.2 Keyword Analysis
23.5.3 Structural Topic Modeling
23.6 Conclusions
References
Part V The Future of AI
24 The Study of Artificial Intelligence as Law
24.1 Introduction
24.2 Legal Technology Today
24.3 AI & Law is Hard
24.4 AI as Law
24.5 Topics in AI
24.5.1 Reasoning
24.5.2 Knowledge
24.5.3 Learning
24.5.4 Language
24.6 Conclusion
References
25 The Right to Mental Integrity in the Age of Artificial Intelligence: Cognitive Human Enhancement Technologies
25.1 Introduction
25.2 Cognitive Human Enhancement Technologies
25.3 Assessing AI-driven Cognitive HETs as Cognitive Extensions
25.4 Mental Integrity in International and European Human Rights Law
25.5 How to Protect Mental Integrity
25.6 Conclusion
References
26 Regulating Artificial General Intelligence (AGI)
26.1 Introduction
26.2 AGI
26.2.1 Concepts
26.2.2 Potential Technological AGI Enablers
26.2.3 Superintelligence
26.2.4 The Societal Consequences and Risks of Superintelligence
26.2.5 AGI Safety
26.2.6 Preliminary Conclusion and Implications for Policymaking
26.3 The Proposed AIA
26.3.1 Risk Management
26.3.2 Human Oversight
26.4 AGI-specific Regulation
26.4.1 AGI Development Procedures
26.4.2 AGI and Human Values
26.4.3 Economic Incentives
26.5 Conclusion
References
27 Influence, Immersion, Intensity, Integration, Interaction: Five Frames for the Future of AI Law and Policy
27.1 Introduction
27.2 Background
27.3 Our Proposed Policy Pivots
27.3.1 Influence
27.3.2 Immersion
27.3.3 Intensity
27.3.4 Integration
27.3.5 Interaction
27.4 The Five Pivots Through the Perspective of Harm
27.5 Concluding Thoughts: Harm through the Five Pivots
References
Index