Overview

Powerful AI. Respectful data. Zero excuses. In AI and Privacy: Navigating Data Ethics Challenges, you'll get a practical, no-nonsense playbook for building AI systems that earn trust-from dataset to deployment. This guide shows you how to ship models that deliver value without violating user privacy, running afoul of regulators, or damaging your brand. Inside, you'll learn how to: Design privacy by default: data minimization, purpose limitation, retention controls, and data contracts that actually stick. Apply privacy-preserving ML at scale: differential privacy, federated learning with secure aggregation, synthetic data, and robust de-identification. Protect sensitive information with cryptographic safeguards: homomorphic encryption and secure multi-party computation. Build resilient systems against AI-specific privacy threats: model inversion, membership inference, and data leakage in LLMs. Operationalize compliance across regions (GDPR, CCPA/CPRA, HIPAA): DPIAs, DSAR workflows, RoPA, consent management, and audit-ready evidence. Integrate privacy with MLOps: lineage, reproducibility, governance, red-teaming, monitoring, incident response, and human-in-the-loop approvals. Communicate clearly with users: transparent UX, meaningful notices, and controls that reduce risk and improve adoption. Featuring field-tested checklists, templates, and step-by-step workflows, this book turns abstract principles into buildable, defensible practices-so your AI is innovative, compliant, and worthy of trust. Who This Book Is For Privacy & security leaders (DPOs, CISOs, counsel) aligning AI with policy and law ML engineers & data scientists embedding privacy into pipelines and models Product & platform teams accountable for trustworthy, auditable AI Students & researchers seeking a modern, practical foundation in AI privacy Ship AI that respects people-and stands up to scrutiny.

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