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Machine learning for high-risk applications : approaches to responsible AI /
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...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , , |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc.,
2023.
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| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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. |
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| Notas: | Includes index. |
| Descripción Física: | 1 online resource (466 pages) : illustrations |
| ISBN: | 9781098102395 1098102398 1098102401 9781098102401 1098102436 9781098102432 |