AI with Integrity

Beschreibung

This book presents a powerful ethical statement for the integration of Artificial Intelligence (AI) into urban environments, challenging technocratic assumptions by grounding smart city innovation in the principles of fairness, transparency, accountability, sustainability, and privacy. It establishes that intelligent urban systems cannot merely optimize performance—they must be morally legible, socially just, and legally compliant. The book argues that the city of the future must be governed not just by data, but by dignity, not just by efficiency, but by equity. The discussion begins by tackling the structural problem of algorithmic bias in urban AI systems, particularly in predictive policing, public service allocation, and transport optimization. Through a mathematically rigorous framework that includes Adversarial Debiasing, Fairness-Constrained Optimization, and Fairness-Aware Reinforcement Learning, the book introduces adaptive models that correct historical inequalities without compromising performance.

• Introduces fairness-aware AI models for equitable urban resource allocation and governance
• Explores AI accountability frameworks for transparent, ethical urban decision-making
• Presents participatory explainable AI (P-XAI) to empower citizens in smart city governance

This book presents a powerful ethical statement for the integration of Artificial Intelligence (AI) into urban environments, challenging technocratic assumptions by grounding smart city innovation in the principles of fairness, transparency, accountability, sustainability, and privacy. It establishes that intelligent urban systems cannot merely optimize performance—they must be morally legible, socially just, and legally compliant. The book argues that the city of the future must be governed not just by data, but by dignity, not just by efficiency, but by equity. The discussion begins by tackling the structural problem of algorithmic bias in urban AI systems, particularly in predictive policing, public service allocation, and transport optimization. Through a mathematically rigorous framework that includes Adversarial Debiasing, Fairness-Constrained Optimization, and Fairness-Aware Reinforcement Learning, the book introduces adaptive models that correct historical inequalities without compromising performance.

Autor*in

Dimitrios Sargiotis

Themen in »AI with Integrity«

Algorithmic Fairness Explainable AI (XAI) Federated Learning Differential Privacy Fairness-Constrained Reinforcement Learning Risk-Aware Urban Regulation Framework (RAURF) Adversarial Debiasing Participatory Explainable AI (P-XAI) Urban AI Artificial Intelligence (AI) Civic AI Governance Ethical AI Governance

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