Название: Responsible AI: Designing, Building, and Assessing Machine Learning and AI (Early Release)
Автор: Patrick Hall, Rumman Chowdhury
Издательство: O’Reilly Media, Inc.
Год: 2021-05-26
Язык: английский
Формат: epub
Размер: 10.1 MB

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. Authors Patrick Hall and Rumman Chowdhury 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.

Today, Machine Learning (ML) is the most commercially viable subdiscipline of artificial intelligence (AI). ML systems are used to make high-stakes decisions in employment, bail, parole, lending and in many other applications throughout the world’s economies. In a corporate setting, ML systems are used in all parts of an organization - from consumer-facing products, to employee assessments, back-office automation, and more. Indeed, the past decade has brought with it wider adoption of ML technologies. But it has also proven that ML presents risks to it’s operators and consumers. Unfortunately, and like nearly all other technologies, ML can fail - whether by unintentional misuse or intentional abuse.

As of today, the Partnership on AI Incident Database holds over 1,000 public reports of algorithmic discrimination, data privacy violations, training data security breaches and other harmful failures. Such risks must be mitigated before organizations, and the general public, can realize the true benefits of this exciting technology. As of today, this still requires action from people — and not just technicians. Addressing the full range of risks posed by complex ML technologies requires a diverse set of talents, experiences, and perspectives. This holistic risk mitigation approach, incorporating technical practices, business processes, and cultural capabilities, is becoming known as responsible AI.

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

Скачать Responsible AI (Early Release)

Скачать с Turbobit
Скачать с Katfile.com