Responsible Ai Best Practices For Creating Trustworthy Ai Systems Early Release Dr Qinghua Lu Drprof Liming Zhu Prof Jon Whittle Dr Xiwei Xu by Dr. Qinghua Lu; Dr./prof. Liming Zhu; Prof. Jon Whittle; Dr. Xiwei Xu 9780138073947, 9780138073923, 0138073945, 0138073929 instant download after payment.

The first practical guide for operationalizing responsible AI-from multi-level governance mechanisms to concrete design patterns and software engineering techniques.
AI is solving real-world challenges and transforming industries, yet there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guides to a wide range of decision-makers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organization and team level, software engineering best practices, architecture styles and design patterns, system-level techniques connecting code with data and model, and the trade-offs in decisions.
Responsible AI includes a set of practices that technologists (e.g., technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure that the developed AI systems are trustworthy throughout the entire lifecycle and trusted by those who use and rely on them. The book offers guidelines and best practices not just for the AI, which is typically a small part of a larger system, but also for the systems engineering process and organizational governance.
First book of its kind about operationalizing responsible AI from the perspective of the entire software development life cycle, complete with real world case studies.
Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system techniques most used by software companies.
Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering.
Reduce the risk of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry.
Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors.
Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this definitive guide.
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