Название: Machine Learning for High-Risk Applications: Techniques for Responsible AI (10th Early Release)Автор: Patrick Hall, James Curtis, Parul Pandey
Издательство: O’Reilly Media, Inc.
Год: 2023-01-11
Страниц: 350
Язык: английский
Формат: epub
Размер: 21.3 MB
Today, Machine Learning (ML) is the most commercially viable sub-discipline of Artificial Ontelligence (AI). ML systems are used to make high-risk decisions in employment, bail, parole, lending, security and in many other high-impact applications throughout the world’s economies and governments. In a corporate setting, ML systems are used in all parts of an organization — from consumer-facing products, to employee assessments, to back-office automation, and more. Indeed, the past decade has brought with it even wider adoption of ML technologies. But it has also proven that ML presents risks to it’s operators, consumers, and even the general public. Machine Learning for High-Risk Applications will arm practitioners with a solid understanding of model risk management processes and new ways to use common Python tools for training explainable models and debugging them for reliability, safety, bias management, security and privacy issues.
The past decade has witnessed a wide adoption of Artificial Intelligence and Machine Learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight.
Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes responsible AI, a holistic approach 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.
It's an ambitious undertaking that requires a diverse set of talents, experiences, and perspectives. Data scientists and nontechnical oversight folks alike need to be recruited and empowered to audit and evaluate high-impact AI/ML systems. Author Patrick Hall created this guide for a new generation of auditors and assessors who want to make AI systems better for organizations, consumers, and the public at large.
Learn how to create a successful and impactful responsible AI practice
Get a guide to existing standards, laws, and assessments for adopting AI technologies
Look at how existing roles at companies are evolving to incorporate responsible AI
Examine business best practices and recommendations for implementing responsible AI
Learn technical approaches for responsible AI at all stages of system development
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