Machine Learning for High-Risk Applications: Approaches to Responsible AI

A field guide for designing, building, and assessing machine learning and AI systems. The book addresses the risks of AI failures, such as algorithmic discrimination and data privacy violations, which have become more common with wider adoption of ML technologies. It outlines a holistic approach to risk mitigation, combining technical practices, business processes, and cultural capabilities for responsible AI. The text covers topics from legal obligations and organizational competencies to technical debugging methods and strategies for safe deployment of AI systems.

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