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

Hall, Patrick; Curtis, James; Pandey, Parul

Brossura

ISBN 10: 1098102436 ISBN 13: 9781098102432

Casa editrice: O'Reilly Media, 2023

Riassunto

The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.

This book describes approaches to responsible AI--a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

• Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
• Learn how to create a successful and impactful AI risk management practice
• Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
• Engage with interactive resources on GitHub and Colab

Dettagli dell?edizione

• Editore: O'Reilly Media
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1098102436
• ISBN 13: 9781098102432
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 466
• Contatto del produttore: non disponibile