BUILDING RESPONSIBLE AI ALGORITHMS : a framework for transparency, fairness, safety, privacy,... and robustness

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will Learn • Build AI/ML models using Responsible AI frameworks and processes • Document information on your datasets and improve data quality • Measure fairness metrics in ML models • Identify harms and risks per task and run safety evaluations on ML models • Create transparent AI/ML models • Develop Responsible AI principles and organizational guidelines Who This Book Is For AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms Table of Contents About the Author About the Technical Reviewer Introduction Part I: Foundation Chapter 1: Responsibility Avoiding the Blame Game Being Accountable Eliminating Toxicity Thinking Fairly Protecting Human Privacy Ensuring Safety Summary Chapter 2: AI Principles Fairness, Bias, and Human-Centered Values Google The Organisation for Economic Cooperation and Development (OECD) The Australian Government Transparency and Trust Accountability Social Benefits Privacy, Safety, and Security Summary Chapter 3: Data The History of Data Data Ethics Ownership Data Control Transparency Accountability Equality Privacy Intention Outcomes Data Curation Best Practices Annotation and Filtering Rater Diversity Synthetic Data Data Cards and Datasheets Model Cards Tools Alternative Datasets Summary Part II: Implementation Chapter 4: Fairness Defining Fairness Equalized Odds Equal Opportunity Demographic Parity Fairness Through Awareness Fairness Through Unawareness Treatment Equality Test Fairness Counterfactual Fairness Fairness in Relational Domains Conditional Statistical Parity Types of Bias Historical Bias Representation Bias Measurement Bias Aggregation Bias Evaluation Bias Deployment Bias Measuring Fairness Fairness Tools Summary Chapter 5: Safety AI Safety Autonomous Learning with Benign Intent Human Controlled with Benign Intent Human Controlled with Malicious Intent AI Harms Discrimination, Hate Speech, and Exclusion Information Hazards Misinformation Harms Malicious Uses Human-Computer Interaction Harms Environmental and Socioeconomic Harms Mitigations and Technical Considerations Benchmarking Summary Chapter 6: Human-in-the-Loop Understanding Human-in-the-Loop Human Annotation Case Study: Jigsaw Toxicity Classification Rater Diversity Case Study: Jigsaw Toxicity Classification Task Design Measures Results and Conclusion Risks and Challenges Summary Chapter 7: Explainability Explainable AI (XAI) Implementing Explainable AI Data Cards Model Cards Open-Source Toolkits Accountability Dimensions of AI Accountability Governance Structures Data Performance Goals and Metrics Monitoring Plans Explainable AI Tools Summary Chapter 8: Privacy Privacy Preserving AI Federated Learning Digging Deeper Differential Privacy Differential Privacy and Fairness Tradeoffs Summary Chapter 9: Robustness Robust ML Models Sampling Bias Mitigation (Preprocessing) Data Balancing Data Augmentation Cross-Validation Ensembles Bias Mitigation (In-Processing and Post-Processing) Transfer Learning Adversarial Training Making Your ML Models Robust Establish a Strong Baseline Model Use Pretrained Models and Cloud APIs Use AutoML Make Model Improvements Model Challenges Data Quality Model Decay Feature Stability Precision versus Recall Input Perturbations Summary Part III: Ethical Considerations Chapter 10: AI Ethics Ethical Considerations for Large Language Models Prevalent Discriminatory Language in LLMs Working with Crowdworkers Inequality and Job Quality Impact on Creatives Disparate Access to Language Model Benefits Ethical Considerations for Generative Models Deepfake Generation Truthfulness, Accuracy, and Hallucinations Copyright Infringement Ethical Considerations for Computer Vision Issues of Fraud Inaccuracies Consent Violations Summary Appendix A: References Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Index