Multidisciplinary Perspectives on Artificial Intelligence and the Law.

Multidisciplinary Perspectives on Artificial Intelligence and the Law.

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Bibliographic Details
Superior document:Law, Governance and Technology Series ; v.58.
:
Sousa Antunes, Henrique.
TeilnehmendeR:
Freitas, Pedro Miguel.
Oliveira, Arlindo L.
Martins Pereira, Clara.
Vaz de Sequeira, Elsa.
Barreto Xavier, Luís.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2024.
©2024.
Year of Publication:2024
Edition:First edition.
Language:English
Series:Law, Governance and Technology Series
Physical Description:1 online resource (457 pages)
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Table of Contents:
  • Intro
  • 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 &amp
  • 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.

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