Data Ethics in the Age of AI
1st Edition
Published on 17. February 2025
327 pages
978-1-5015-2263-5 (ISBN)
Description
In a world increasingly shaped by data and artificial intelligence, the ethical challenges surrounding data use have never been more critical. Data Ethics in the Age of AI provides a compelling exploration of the moral, legal, and societal dimensions of data in the digital era.
This insightful book delves into key issues such as privacy, algorithmic bias, transparency, and accountability, offering readers a robust framework for navigating the complex intersection of technology and ethics. With real-world examples and thought-provoking discussions, it equips business leaders, policymakers, researchers, and students with the knowledge and tools to foster ethical responsibility in the face of rapid technological advancements.
Discover how we can build a future where data is used responsibly, equitably, and for the greater good. Data Ethics in the Age of AI is your essential guide to understanding and shaping the ethical landscape of our data-driven world.
This insightful book delves into key issues such as privacy, algorithmic bias, transparency, and accountability, offering readers a robust framework for navigating the complex intersection of technology and ethics. With real-world examples and thought-provoking discussions, it equips business leaders, policymakers, researchers, and students with the knowledge and tools to foster ethical responsibility in the face of rapid technological advancements.
Discover how we can build a future where data is used responsibly, equitably, and for the greater good. Data Ethics in the Age of AI is your essential guide to understanding and shaping the ethical landscape of our data-driven world.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
US School Grade: College Graduate Student
File size
11,54 MB
ISBN-13
978-1-5015-2263-5 (9781501522635)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Arshad Khan
Data Ethics in the Age of AI
Book
02/2025
1st Edition
De Gruyter
€44.95
Article not available at the moment
Person
Arshad Khan is a professor at the University of California, San Diego and previously co-authored the best-selling title Stock Investing for Everyone: Tools For Investing Like The Pros. He has also authored titles on business intelligence, data warehousing, and SAP.
Content
- Half Title
- Title
- Copyright
- Contents
- Preface
- Chapter 1: Introduction to Data Ethics
- The Origins and Evolution of Data Ethics
- Early Foundations of Data Ethics
- The Establishment of Fair Information Practice Principles
- Expanding Ethical Considerations Beyond Privacy
- The Impact of AI on Data Ethics
- Flexibility in Ethical Frameworks
- Toward Ethical Data Practices
- Fostering a Culture of Ethical Awareness
- Engaging Diverse Perspectives in Ethical Dialogue
- Charting the Path Forward
- Evolving Frameworks in Modern Data Ethics
- Defining Contemporary Data Ethics
- The Concept of Data Dignity
- The Ongoing Evolution of Ethical Frameworks
- Adapting to Emerging Technologies
- Organizational Responsibilities in Data Ethics
- The Role of Governments and Regulatory Bodies
- The Role of Civil Society in Data Ethics
- Holistic Approach to Data Ethics
- The Need for Continuous Dialogue
- Promoting Education and Awareness
- Chapter 2: The Importance of Data Ethics
- The Role of Ethics in the Digital Age
- The Impact on Individuals and Society
- Creating a Just and Equitable Society
- The Influence on Public Policy and Regulation
- Shaping the Social Narrative
- Promoting Education and Awareness
- The Significance of Data Ethics Today
- The Need for Collaboration
- Navigating Ethical Challenges in Emerging Technologies
- The Future of Data Ethics
- The Role of Data Ethics in Shaping Modern Technologies
- Data Ethics in Modern Technologies
- Addressing Bias in AI
- Ethical Implications in High-Stakes Contexts
- The Role of Data Ethics in IoT
- Proactive Approach to Data Ethics
- Driving Innovation Through Data Ethics
- Ethical Considerations as a Core Principle
- Education and Awareness Initiatives
- Looking to the Future with Data Ethics
- Chapter 3: Core Ethics in Data Governance
- Overview of Key Concepts and Frameworks
- Privacy
- Consent
- Accountability
- Interconnection: Privacy, Consent, and Accountability
- Societal Implications
- Building an Ethical Digital Future
- The Future of Ethical Data Practices
- Education and Awareness Initiatives
- Toward a Trustworthy Digital Future
- Balancing Ethics in Data Governance
- Deontological Ethics
- Regulatory Implications of Deontological Ethics
- Consequentialism in Data Governance
- Integrating Ethical Approaches for a Balanced Framework
- Concluding Thoughts on Ethical Data Governance
- Chapter 4: Ethical Principles in Data Collection
- Informed Consent
- Definition of Informed Consent
- Protecting Individual Autonomy
- Building Trust and Credibility for Organizations
- Legal Requirements and Compliance
- Protecting Individual Rights amid Technological Advances
- Empowering Individuals and Enhancing Data Governance
- Informed Consent Challenges: Practical Issues
- Challenges in Informed Consent
- Complexity of Communication
- Implicit Consent and Dark Patterns
- Challenges with IoT Devices
- Evolving Data Practices and Continuous Consent
- The Role of User Education and Regulatory Support
- Data Ownership
- Empowering Individuals Through Data Ownership
- Empowerment Through Informed Decision-Making
- Ethical Implications of Data Ownership
- Building Trust Through Data Ownership
- Strategic Advantage for Businesses
- Toward Ethical Data Governance
- Navigating Ownership and Business Usage Ethics
- Balancing Ownership and Business Needs
- Transparent Data-Handling Practices
- Shift to Data Stewardship
- Transparency and Accountability
- The Role of Education in Ethical Data Management
- Toward an Ethical Data Ecosystem
- Chapter 5: Privacy in Data Ethics
- Privacy in the Digital Era
- The Evolution of Privacy
- Informational Self-Determination and Autonomy
- Managing the Digital Footprint
- Protecting Sensitive Data and Addressing Vulnerabilities
- Transparency and Consent in Data Practices
- Collective Effort for Ethical Privacy
- Consequences of Privacy Breaches
- The Impact of Privacy Breaches on Individuals and Organizations
- Consequences for Individuals
- The Impact on Organizations
- Legal and Financial Ramifications
- Broader Community and Regulatory Implications
- Cultural and Societal Effects
- Ensuring Ethical Responsibility in Data Privacy
- Chapter 6: Security in Data Ethics
- Effective Data Security Methods
- The Importance of Data Security
- Encryption: A Fundamental Security Measure
- Strong Authentication Mechanisms
- Principle of Least Privilege and Security Audits
- Employee Training and Incident Response Planning
- Physical Security Measures
- Data Backup
- Building an Ethical Data Security Framework
- Balancing Security, Access, and Transparency
- Ethical Dilemmas in Data Security
- Surveillance Technologies and Privacy Concerns
- Balancing Security Controls with User Access
- Transparency in Data Protection Practices
- The Social Impact of Security Measures
- Engaging Stakeholders and Fostering Ethical Practices
- Building an Ethical Framework for Data Security
- Chapter 7: Ethical Data Usage and Minimization
- Guiding Principles of Ethical Data Use
- Upholding Trust: Ethics of Data Usage
- Data Integrity Matters
- Preventing Exploitation of Data
- Implementing Robust Internal Policies
- Continuous Evaluation and Adaptation
- The Cornerstone of Data Ethics
- Ethical and Unethical Data Usage
- Ethical Data Use
- Examples of Ethical Data Usage
- Examples of Unethical Data Usage
- The Impact of Ethical vs. Unethical Data Use
- The Impact of Ethical Data Practices on Trust and Innovation
- Commitment to Ethical Data Practices
- Data Minimization
- Data Minimization: The Key to Ethical Data Practices
- Protecting Individual Privacy
- Respecting Autonomy and Rights
- Streamlining Data Practices
- Competitive Advantage in Ethical Practices
- The Moral Imperative of Data Minimization
- Challenges of Data Minimization
- Balancing Act: Overcoming Hurdles in Data Minimization
- The Complexity of Data Ecosystems
- Legacy Systems and Business Practices
- Cultural Resistance to Change
- Regulatory Compliance Complexities
- Customer Perceptions and Expectations
- Navigating Challenges to Uphold Ethical Standards
- Chapter 8: Data Bias
- Understanding Data Bias
- Types of Bias
- Selection Bias
- Algorithmic Bias
- Confirmation Bias
- Other Types of Bias
- Measurement Bias
- Survivorship Bias
- Recall Bias
- Broader Implications of Bias
- Value-Laden Bias
- The Influence of Values and Beliefs
- The Choice of Data and Metrics
- Interpretation and Communication
- Bias and Its Consequences
- The Ethical and Societal Impact of Flawed Data
- The Impact on Individual Decision-Making
- Societal Implications of Bias
- Economic Consequences of Bias
- Implications for Public Policy
- Addressing Bias for Ethical Data Practices and Decision-Making
- Chapter 9: Data Bias Mitigation
- Strategies for Fairness
- Mitigating Bias in Data-Driven Decision-Making
- Data Auditing as a Foundational Technique
- Algorithmic Auditing for Bias Identification
- Diverse Data Practices for Mitigation
- Bias Correction Techniques
- Cultivating a Culture of Transparency and Accountability
- Mitigating Bias for Fair Data Practices
- Practitioners' Role in Promoting Fairness
- The Ethical Responsibility of Practitioners
- Ethical Responsibility in Data and Algorithm Design
- Advocating for Transparency and Accountability
- Prioritizing Inclusivity in Data Practices
- Advocating for Ethical Guidelines and Best Practices
- Chapter 10: Bias in Computer Systems
- Understanding Bias in Computer Systems
- Unpacking Bias in Computer Systems
- Biased Training Data
- Lack of Diversity in Development Teams
- The Societal Impact of Algorithmic Bias
- Mitigating Bias in Computer Systems: A Dual Approach
- Community Engagement and Participatory Design
- Accountability and Ethical Considerations
- Toward Fairer Computer Systems
- Framework for Analyzing Bias in Computer Systems
- Preexisting Bias
- Bias Originating from Individuals
- Bias Originating from Society
- Addressing Preexisting Bias
- Technical Bias
- Emergent Bias
- Addressing the Complexity of Bias in Computer Systems
- Chapter 11: Transparency and Accountability
- Transparency in Data Practices
- Navigating Data Ethics: The Role of Transparency
- Transparency for Trust
- Promoting Accountability Through Transparency
- Empowering Individuals Through Transparency
- Business Case for Transparency in Data Practices
- The Cornerstone of Ethical Data Practices
- Exploring Barriers to Data Transparency
- Navigating Obstacles
- The Complexity of Data Systems and Algorithms
- Organizational Reluctance to Share Information
- Challenges from Data Privacy Regulations
- Limited Public Understanding of Data
- Resource Constraints
- Overcoming Challenges to Build Trust
- Accountability in Ethical Data Use
- Assigning Responsibility
- The Role of Data Practitioners
- Leadership Accountability in Ethical Data Usage
- The Role of Legal and Compliance Teams
- The Role of Regulators and Policymakers
- Societal Influence on Ethical Data Usage
- Collective Responsibility in Ethical Data Practices
- Tools for Ensuring Data Accountability
- Building Accountability in Data Management
- Technical Tools for Accountability
- Audit Trails
- Algorithmic Transparency and Explainability
- Organizational Practices for Accountability
- Data Governance Frameworks
- Ethical Impact Assessments
- Training and Education for Data Practitioners
- Regulatory Measures and Compliance
- Fostering a Culture of Accountability
- Chapter 12: Ethical AI: Tackling Bias and Fairness
- Bias in AI
- Bias in AI: Sources and Solutions
- Biased Training Data
- Algorithmic Design Choices
- Human Involvement in the AI Lifecycle
- Societal Inequality
- Addressing Bias in AI
- Ensuring Fairness in AI
- The Importance of Fairness in AI
- Diverse and Representative Datasets
- Bias Detection and Mitigation Tools
- Transparency and Explainability
- Stakeholder Involvement and Governance
- Monitoring and Updating AI Systems
- Building Ethical AI Systems
- Accountability for AI Decisions
- Complexity of Accountability in AI Systems
- Decoding Decision-Making in Black-Box AI Models
- Shared Responsibility Among Stakeholders
- Regulatory and Ethical Uncertainty
- Algorithmic Bias and Unintended Consequences
- Navigating Challenges in AI Responsibility
- Toward a More Accountable AI Landscape
- Ethical AI Governance Frameworks
- The Importance of Ethical AI Governance
- Risk Management and Impact Assessment
- AI Ethics Boards and Advisory Councils
- Transparency and Explainability Tools
- Continuous Monitoring and Auditing
- Stakeholder Engagement
- Fostering Ethical AI Development
- Chapter 13: Ethical Implications of AI in Decision-Making Systems
- AI in Autonomous Decision-Making
- AI's Expanding Role in Critical Sectors
- Ethical Concerns in Life-Critical Decisions
- Bias and Discrimination in Automated Decisions
- Human-AI Collaboration in Decisions
- The Role of Human Oversight in AI Systems
- Augmenting Human Judgment with AI
- Ethical Dilemmas in Overriding AI Decisions
- Bias from Human-AI Interactions
- Algorithmic Transparency and Explainability
- The Black-Box Problem in AI
- The Importance of Explainability in AI Systems
- Techniques for Improving AI Explainability
- Balancing Complexity and Usability
- Accountability for Automated Decisions
- Defining Responsibility in AI-Driven Decision-Making
- Accountability for AI Decisions
- Shared Responsibility Between Developers and Users
- Ethical Frameworks for Assigning Liability
- Addressing Ethical Failures in AI
- The Impact on Workforce and Decision Roles
- AI's Impact on Workforce Roles
- AI's Impact on Traditional Decision-Making Roles
- Automation vs. Human Expertise
- Job Displacement and Reskilling in AI-Era Decision Systems
- Ensuring Ethical Use of AI in Workforce Decisions
- Chapter 14: Ethical Implications of Big Data Analytics
- The Benefits of Big Data Analytics for Society
- Transforming Society with Big Data
- Advancements in Healthcare and Agriculture
- Improvements in Public Services and Decision-Making
- Fostering Economic Growth and Business Innovation
- Big Data Usage Risks and Management
- Risks
- Algorithmic Discrimination and Its Impact
- Lack of Transparency and Accountability
- Balancing Opportunities and Risks
- Ethical Risks: Privacy Breaches and Misuse
- Ethical Risks of Big Data
- Privacy Breaches and Their Consequences
- Data Misuse
- Addressing Ethical Risks
- Informed Consent in Big Data
- The Ethical Imperatives of Informed Consent in Big Data
- Navigating the Complexity of Consent in Big Data
- Challenges Due to the Scale of Data Collection
- Understanding the Scope of Data Usage and Consent Implications
- The Dynamic Nature of Data and the Need for Ongoing Consent
- Power Asymmetry in Data Collection
- Rethinking Consent Practices
- Techniques for Ethical Consent Management in Data Practices
- Use Clear and Concise Communication
- Adopt Dynamic Consent Mechanisms
- Contextualize Consent
- Enhance Consent Through Educational Initiatives
- Leverage Technology to Enhance Consent Management
- Build Trust Through Ethical Consent Practices
- Chapter 15: Data Ethics in Business Contexts
- Corporate Data Responsibility
- Businesses' Role in Ethical Data Use
- Committing to Transparency
- Prioritizing Data Security
- Embracing Data Minimization
- Ensuring Fairness and Non-Discrimination
- Advocating for Ethical Data Practices
- Ethical Data Responsibility for Businesses
- Ethical and Unethical Practices: Industry Case Studies
- Ethical Practices
- Unethical Practices
- Lessons Learned
- Balancing Profitability with Ethical Practice
- Navigating Ethical Dilemmas
- Understanding the Profitability-Ethics Dichotomy
- Effective Strategies for Balancing Profit and Ethics
- Balancing Ethics and Profits
- Corporate Social Responsibility: An Ethical Approach
- Patagonia: Environmental Stewardship
- Ben & Jerry's: Social Justice Advocacy
- Microsoft: Commitment to Accessibility
- Unilever: Sustainable Sourcing and Community Impact
- The Impact of Ethical Practices on Business Success
- Chapter 16: Regulatory Frameworks and Compliance
- Key Data Regulations
- General Data Protection Regulation
- California Consumer Privacy Act
- Other Major Data Laws
- Children's Online Privacy Protection Act (COPPA)
- Health Insurance Portability and Accountability Act
- Personal Information Protection and Electronic Documents Act (PIPEDA)
- Brazilian General Data Protection Law (LGPD)
- Data Protection Act 2018
- Ensuring Ethical Data-handling
- Regulations and Ethical Data Use
- Transforming Corporate Behavior
- Driving Ethical Practices
- Enhancing Accountability and Responsibility
- Fostering Technological Innovations
- Cultural Shift Toward Ethical Data Use
- Catalysts for Ethical Change
- Compliance Strategies
- Regulatory Compliance Best Practices
- Conduct Comprehensive Data Audits
- Implement Robust Data Governance Policies
- Invest in Employee Training and Awareness
- Utilize Technology Solutions for Compliance
- Establish a Culture of Compliance and Ethics
- Navigating Data Compliance
- Compliance Challenges
- Navigating the Complexities of Data Compliance
- The Complexity and Ambiguity of Regulations
- Resource Constraints
- Integration of Compliance into Existing Systems
- Cultural Resistance to Change
- Balancing Business Objectives with Compliance Needs
- Navigating Compliance Challenges
- Chapter 17: The Future of Data Ethics
- Emerging Trends in Data Ethics
- The Impact of Emerging Technologies on Data Ethics
- The Ethical Impact of AI
- Quantum Computing: New Frontiers and Ethical Implications
- The Interplay Between AI and Quantum Computing
- Establishing Ethical Frameworks for Emerging Technologies
- Conclusion: Ethics in Emerging Technologies
- Future Ethical Dilemmas in Data Ethics
- The Rise of Autonomous Systems
- Surveillance and Data Privacy
- Manipulation Through Data and AI
- Data Ownership and Control
- Environmental Considerations in Data Ethics
- Concluding Reflections
- Building Ethical Data Cultures
- Empowering Ethical Data Practices Through Education
- Cultivating Awareness of Data Ethics
- Embedding Data Ethics Across Educational Curricula
- Fostering Critical Thinking for Ethical Decision-Making
- Cultivating a Culture of Ethical Responsibility
- Key Takeaways for Ethical Data Practices
- Establishing Ethical Frameworks for Responsible Data Practices
- Key Components of an Ethical Framework
- Collaborating with Stakeholders in Framework Development
- Building Flexible and Dynamic Ethical Frameworks
- Effective Implementation and Communication of the Ethical
- Framework
- Key Insights on Ethical Frameworks
- Appendix: Case Studies
- Criminal Justice and Law Enforcement
- COMPAS Recidivism Algorithm
- Machine Bias: Case Study of Brisha Borden
- Accused by an Algorithm
- Autonomous Driving
- Uber Self-Driving Car Fatality
- Tesla Autopilot Crashes
- Predictive Analytics
- Predictive Analytics Can Be the Problem
- Zillow's Algorithm for Predicting Market Trends
- Gender Discrimination
- Amazon's AI Recruitment Tool
- Apple Card Credit Limit Discrimination
- Employment
- Hiring by an Algorithm
- Amazon's Flex Program Algorithm
- Healthcare and Medical Applications
- Google Health's AI in Breast Cancer Detection
- Healthcare Algorithm Racial Bias
- Stanford Vaccine Algorithm
- Social Media and Algorithmic Influence
- Facebook's Ad Targeting
- YouTube's Algorithm Promoting Extremism
- Microsoft Tay Chatbot
- Twitter's Image-Cropping Algorithm
- Google Photos Misclassification
- Addiction by Design Using Adaptive Algorithms
- Facial Recognition and Ethical Concerns
- Clearview AI Facial Recognition
- PimEyes: Navigating the Ethics of Facial Recognition
- Algorithmic Decision-Making in Education
- Navigate Risk: Predictive Analytics and Educational Steering
- UK A-Level Grading Algorithm
- Secret Teacher Evaluation System
- Privacy and Ethics
- AcurianHealth-Walgreens Privacy Controversy
- Ethics in AI: The Case of Dr. Timnit Gebru
- Index
