Ethical governance of artificial intelligence in the public sector / Liza Ireni-Saban and Maya Sherman.

1. Ethics of Algorithms. The Role of Algorithms in Decision-Making. Key Concepts of Ethical Algorithms. Inconclusive Evidence
Inscrutable Evidence
Misguided Evidence
Unfair Outcomes
Transformative Effects
Traceability
Ethical Implications of Algorithmic Design. Algorithmic Inconclusive Evidence Guides Unjustified Actions
Transparency and Interpretability of Algorithms Is Infeasible
Biased Decisions By Algorithms
Profiling Algorithms Leading to Discrimination
Algorithm's Threat to Autonomy
Challenges for Informational Privacy
Traceability as a Path to Moral Responsibility
2. Overview of AI Taxonomy and Technological Methods
Between AI and Intelligence
National Security Perspective of AI Taxonomy
AI Techniques and Evolutionary Stages (ANI, AGI, & ASI)
Algorithmic Bias
3. Developing Ethical Framework for Ethical Responsible Innovation
Governance of Responsible Innovation
Ethical Approaches to Responsible Innovation: Deontology and Consequentialism

Prudential Ethics and Responsible Innovation. The Wisdom of Prudence in Politics
Reconciliation Project-Justification for Ethical Egoism in Politics
What Is Ethical Egoism?
Reconciliation Project: Ethics and Self-Interest Reconciled With Prudential Reasoning
References
4. Prudential Ethics and Intersectionality: Towards a Normative Framework for AI Applications. Origins of Intersectionality. Development of Intersectionality Theory
Enhancing Prudential Ethics With Intersectionality
Incorporating Intersectionality and Prudential Ethics Into AI Policymaking and Application

5. Contemporary Analysis of AI Affairs in the International Arena. Cultural Perspectives of Surveillance and Facial Recognition Technologies
Influence Operations and AI Threats to the Democratic Ethos
AI-driven Disinformation 2.0
Deepfake
Between AI Technologies and the COVID-19 Pandemic
Critical Approach to AI Governance in the Public Sector