Ethical Artificial Intelligence in the Insurance Industry

Nyhet

Balancing Efficiency, Fairness, and Risk

Inbunden, Engelska, 2026

2 049 kr

Kommande

Provides an overview of the challenges and opportunities presented by AI across the insurance value chain. As insurers rapidly integrate machine learning, deep learning, and predictive analytics into underwriting, claims processing, fraud detection, and pricing, the need for robust ethical frameworks and responsible AI governance has become paramount. The book serves as a foundational guide for developing AI systems in insurance that are not only efficient but also equitable and socially responsible.Algorithmic structures and data pipelines that shape modern insurance systems, that review potential sources of bias, opacity, and inequality are examined. The book addresses technical, legal, and organizational dimensions of ethical AI adoption—ranging from explainability and accountability mechanisms to data privacy, informed consent, and inclusion.The book is targeted at professionals, scholars, data scientists, actuaries, and policymakers.

Produktinformation

Utgivningsdatum2026-07-14
Mått178 x 254 x undefined mm
FormatInbunden
SpråkEngelska
Antal sidor308
FörlagTaylor & Francis Ltd
ISBN9781041236887

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Wasswa Shafik (IEEE member) is the Founder and the Team Lead at the Dig Connectivity Research Laboratory (DCRLab) in Kampala, Uganda. He holds a PhD in Digital Science (Computer Science) from Universiti Brunei Darussalam and a Master’s in Information Technology Engineering (Computer and Communication Networks) from Yazd University, Iran. Wasswa also earned a Bachelor’s in Information Technology from Ndejje University, Uganda. He has received specialized training from the National Institutes of Health (NIH), the U.S. Department of Health and Human Services, and the Bloomberg School of Public Health. His research focuses on Artificial Intelligence, Computer Vision, Deep Learning, and Internet of Things applications in Smart Agriculture, Digital Health, and Sustainable Computing.

Preface. 1. The Ethical Imperative in AI-Driven Insurance: Foundations, Motivations, and Risks. 2. Architecting Intelligence: Understanding the AI Value Chain in the Insurance Ecosystem. 3. Navigating Legal, Regulatory, and Ethical Frameworks in Algorithmic Insurance Practices. 4. Algorithmic Underwriting: Addressing Bias, Transparency, and Data Equity in Risk Assessment. 5. AI-Enabled Claims Management: Automation, Fairness, and Human-AI Collaboration. 6. Combating Insurance Fraud with AI: Balancing Predictive Power and Ethical Constraints. 7. Dynamic Pricing and Personalization: Ethical Implications of Behavioral and Big Data in Premium Models. 8. Detecting and Mitigating Algorithmic Bias: Technical and Ethical Interventions in Insurance AI. 9. Explainable AI for Insurance: Enhancing Transparency, Accountability, and Client Trust. 10. Data Governance in Insurance AI: Ensuring Privacy, Consent, and Ethical Data Life Cycles. 11. Embedding Human Oversight in Automated Systems: Toward Accountable and Trustworthy AI. 12. Equity and Inclusion in Insurance AI: Expanding Access for Marginalized and Underserved Populations. 13. Global Perspectives on Ethical Insurance AI: Comparative Case Studies and Regional Challenges. 14. Designing an Ethical AI Strategy for Insurers: Policies, Practices, and Organizational Transformation. 15. Toward a Human-Centric Future: Strategic Road Maps for Sustainable and Ethical AI Integration in Insurance. About the Author.