Responsible AI in the Enterprise : Practical AI Risk Management for Explainable, Auditable, and Safe Models with Hyperscalers and Azure OpenAI

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 eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book 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,... Responsible AI in the Enterprise Foreword Foreword 2 Contributors About the authors About the reviewers Preface Who this book is for Essential chapters tailored to distinct AI-related positions What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book Part 1: Bigot in the Machine – A Primer 1 Explainable and Ethical AI Primer The imperative of AI governance Key terminologies Explainability Interpretability Explicability Safe and trustworthy Fairness Ethics Transparency Model governance Enterprise risk management and governance Tools for enterprise risk governance AI risk governance in the enterprise Perpetuating bias – the network effect Transparency versus black-box apologetics – advocating for AI explainability The AI alignment problem Summary References and further reading 2 Algorithms Gone Wild AI in hiring and recruitment Facial recognition Bias in large language models (LLMS) Hidden cost of AI safety – low wages and psychological impact AI-powered inequity and discrimination Policing and surveillance Social media and attention engineering The environmental impact Autonomous weapon systems and military The AIID Summary References and further reading Part 2: Enterprise Risk Observability Model Governance 3 Opening the Algorithmic Black Box Getting started with interpretable methods The business case for explainable AI Taxonomy of ML explainability methods Shapley Additive exPlanations How is SHAP different from Shapley values? A working example of SHAP Local Interpretable Model-Agnostic Explanations A working example of LIME Feature importance Anchors PDPs Counterfactual explanations Summary References and further reading 4 Robust ML – Monitoring and Management An overview of ML attacks and countermeasures Model and data security Privacy and compliance Attack prevention and monitoring Ethics and responsible AI The ML life cycle Adopting an ML life cycle MLOps and ModelOps Model drift Data drift Concept drift Monitoring and mitigating drift in ML models Simple data drift detection using Python data drift detector Housing price data drift detection using Evidently Analyzing data drift using Azure ML Summary References and further reading 5 Model Governance, Audit, and Compliance Policies and regulations United States European Union United Kingdom Singapore United Arab Emirates Toronto Declaration – protecting the right to equality in ML Professional bodies and industry standards Microsoft’s Responsible AI framework IEEE Global Initiative for Ethical Considerations in AI and Autonomous Systems ISO/IEC’s standards for AI OECD AI Principles The University of Oxford’s recommendations for AI governance PwC’s Responsible AI Principles/Toolkit Alan Turing Institute guide to AI ethics Technology toolkits Microsoft Fairlearn IBM’s AI Explainability 360 open source toolkit Credo AI Lens toolkit PiML – the integrated Python toolbox for interpretable ML FAT Forensics – algorithmic fairness, accountability, and transparency toolbox Aequitas – the Bias and Fairness Audit Toolkit AI trust, risk, and security management Auditing checklists and measures Datasheets for datasets Model cards for model reporting Summary References and further reading 6 Enterprise Starter Kit for Fairness, Accountability, and Transparency Getting started with enterprise AI governance AI STEPS FORWARD – AI governance framework Implementing AI STEPS FORWARD in an enterprise The strategic principles of AI STEPS FORWARD AI STEPS FORWARD in enterprise governance The AI STEPS FORWARD maturity model Risk management in AI STEPS FORWARD Measures and metrics of AI STEPS FORWARD AI STEPS FORWARD – taxonomy of components Salient capabilities for AI Governance The indispensable role of the C-suite in fostering responsible AI adoption An AI Center of Excellence The role of internal AI boards in enterprise AI governance Healthcare systems Retail and e-commerce systems Financial services Predictive analytics and forecasting Cross-industry applications of AI Establishing repeatable processes, controls, and assessments for AI systems Ethical AI upskilling and education Summary References and further reading Part 3: Explainable AI in Action 7 Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360 Getting started with hyperscaler interpretability toolkits Google Vertex Explainable AI Model interpretability in Vertex AI – feature attribution and example-based explanations Integration with Google Colab and other notebooks Simplified deployment Explanations are comprehensive and multimodal AWS Sagemaker Clarify Azure Machine Learning model interpretability Azure’s responsible AI offerings Responsible AI scorecards Open source offerings – the responsible AI toolbox Open source toolkits and lenses IBM AI Fairness 360 Aequitas – Bias and Fairness Audit Toolkit PETs Differential privacy Homomorphic encryption Secure multiparty computation Federated learning Data anonymization Data perturbation Summary References and further reading 8 Fairness in AI Systems with Microsoft Fairlearn Getting started with fairness Fairness metrics Fairness-related harms Getting started with Fairlearn Summary References and further reading 9 Fairness Assessment and Bias Mitigation with Fairlearn and the Responsible AI Toolbox Fairness metrics Demographic parity Equalized odds Simpson’s paradox and the risks of multiple testing Bias and disparity mitigation with Fairlearn Fairness in real-world scenarios Mitigating correlation-related bias The Responsible AI Toolbox The Responsible AI dashboard Summary References and further reading 10 Foundational Models and Azure OpenAI Foundation models Bias in foundation models The AI alignment challenge – investigating GPT-4’s power-seeking behavior with ARC Enterprise use of foundation models and bias remediation Biases in GPT3 Azure OpenAI Access to Azure OpenAI The Code of Conduct Azure OpenAI Service content filtering Use cases and governance What not to do – limitations and potential risks Data, privacy, and security for Azure OpenAI Service AI governance for the enterprise use of Azure OpenAI Getting started with Azure OpenAI Consuming the Azure OpenAI GPT3 model using the API Azure OpenAI Service models Code generation models Embedding models Summary References and further reading Index Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase 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.