Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI (en Inglés)
Reseña del libro "Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI (en Inglés)"
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfallsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning modelsBook DescriptionResponsible 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.What you will learnUnderstand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platformsWho this book is forThis book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.Table of ContentsA Primer on Explainable and Ethical AIAlgorithms Gone Wild - Bias's Greatest HitsOpening the Algorithmic BlackboxOperationalizing Model MonitoringModel Governance - Audit, and Compliance Standards & RecommendationsEnterprise Starter Kit for Fairness, Accountability and TransparencyInterpretability Toolkits and Fairness MeasuresFairness in AI System with Microsoft FairLearnFairness Assessment and Bias Mitigation with FairLearn and Responsible AI ToolboxFoundational Models and Azure OpenAI
