Machine Learning for High-Risk Applications
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. 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 holi… Celý popis ↓
Porovnanie cien Machine Learning for High-Risk Applications
Popis Machine Learning for High-Risk Applications
Machine Learning for High-Risk Applications
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. 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. 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.
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
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