Responsible AI in the Enterprise

Responsible AI in the Enterprise offers a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts like explainable, safe, ethical, robust, transparent, auditable, and interpretable machine learning models, this book equips developers with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Readers will gain an in-depth understanding of FairLearn and InterpretML, as well as other tools like Google's What-If Tool, ML Fairness Gym, IBM's AI 360 Fairness tool, Aequitas, and FairLearn. The book covers various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance standards recommendations. It provides practical insights on how to use AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Readers will explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, and learn how to use FairLearn for fairness assessment and bias mitigation. By the end of this book you will ge to grips with tools and techniques available to create transparent and accountable machine learning models.