Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance.
Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.
By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.

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Spis treści

Responsible AI in the Enterprise. Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI eBook -- spis treści

• 1. A Primer on Explainable and Ethical AI
• 2. Algorithms Gone Wild - Bias's Greatest Hits
• 3. Opening the Algorithmic Blackbox
• 4. Operationalizing Model Monitoring
• 5. Model Governance - Audit, and Compliance Standards & Recommendations
• 6. Enterprise Starter Kit for Fairness, Accountability and Transparency
• 7. Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360
• 8. Fairness in AI System with Microsoft FairLearn
• 9. Fairness Assessment and Bias Mitigation with FairLearn and Responsible AI Toolbox
• 10. Foundational Models and Azure OpenAI