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دانلود کتاب Practical Fairness
دانلود کتاب انصاف عملی
مشخصات کتاب
Practical Fairness
ویرایش:
نویسندگان: Aileen Nielsen
سری:
ISBN (شابک) : 9781492075738
ناشر: O'Reilly Media, Inc.
سال نشر: 2020
تعداد صفحات:
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 4 Mb
قیمت کتاب (تومان) : 52,000
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توضیحاتی درمورد کتاب به خارجی
فهرست مطالب
Copyright Table of Contents Preface Goals of This Book Practical Notes on the Book Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments Chapter 1. Fairness, Technology, and the Real World Fairness in Engineering Is an Old Problem Our Fairness Problems Now Community Norms Equity and Equality Security Privacy Legal Responses to Fairness in Technology The Assumptions and Approaches in This Book What If I’m Skeptical of All This Fairness Talk? Won’t Fairness Slow Down Innovation? Are There Any Real-World Consequences for Not Developing Fairness-Aware Practices? What Is Fairness? Rules to Code By Equality and Equity Security Privacy Chapter 2. Understanding Fairness and the Data Science Pipeline Metrics for Fairness Measures of Equity Measures of Privacy Measures of Security Connected Concepts Privacy and Security Privacy and Equity Equality and Security Accuracy and Fairness Automated Fairness? Checklist of Points of Entry for Fairness in the Data Science Pipeline Assembling a Data Set Modeling Interface Concluding Remarks Chapter 3. Fair Data Ensuring Data Integrity True Measurements Proportionality and Sampling Technique Choosing Appropriate Data Equity Privacy Security Case Study: Choosing the Right Question for a Data Set and the Right Data Set for a Question Quality Assurance for a Data Set: Identifying Potential Discrimination A Timeline for Fairness Interventions Comprehensive Data-Acquisition Checklist Concluding Remarks Chapter 4. Fairness Pre-Processing Simple Pre-Processing Methods Suppression: The Baseline Massaging the Data Set: Relabeling AIF360 Pipeline Loading the Data Fairness Metrics The US Census Data Set Suppression Reweighting How It Works Code Demonstration Learning Fair Representations How It Works Code Demonstration Optimized Data Transformations How It Works Code Demonstration Fairness Pre-Processing Checklist Concluding Remarks Chapter 5. Fairness In-Processing The Basic Idea The Medical Data Set Prejudice Remover How It Works Code Demonstration Adversarial Debiasing How It Works Code Demonstration In-Processing Beyond Antidiscrimination Model Selection Concluding Remarks Chapter 6. Fairness Post-Processing Post-Processing Versus Black-Box Auditing The Data Set Equality of Opportunity How It Works Code Demonstration Calibration-Preserving Equalized Odds How It Works Code Demonstration Concluding Remarks Chapter 7. Model Auditing for Fairness and Discrimination The Parameters of an Audit Scoping: What Should We Audit? Black-Box Auditing Running a Model Through Different Counterfactuals Model of the Model Auditing Black-Box Models for Indirect Influence Concluding Remarks Chapter 8. Interpretable Models and Explainability Algorithms Interpretation Versus Explanation Interpretable Models GLRM: How It Works Code Demonstration Explainability Methods SHAP and LIME: The Workhorses for Local Post Hoc Explanations Data-Driven Explanation Explainability Metrics What Interpretation and Explainability Miss Attacks on Explainable Machine Learning Interpretation and Explanation Checklist Concluding Remarks Chapter 9. ML Models and Privacy Membership Attacks How It Works Code Demonstration Other Privacy Problems and Attacks Important Privacy Techniques Concluding Remarks Chapter 10. ML Models and Security Evasion Attacks How It Works Code Demonstration Defending Against Adversarial Attacks Some Evasion Attack Packages Why Do Evasion Attacks Matter to You? Poisoning Attacks How They Work Defenses Against Poisoning Attacks Some Poisoning Attack Packages Why Do Poisoning Attacks Matter to You? Concluding Remarks Chapter 11. Fair Product Design and Deployment Reasonable Expectations Expectations of Moving Targets Clear Communication Fiduciary Obligations Respecting Traditional Spheres of Privacy and Private Life Value Creation Complex Systems The Impact of the Product Life Cycle The Need for Record Keeping The Need for Experts Clear Security Promises and Delineated Limitations Reasonable Expectations of Security Possibility of Downstream Control and Verification Verification Systems and Obligations Product Iteration Timelines Tracking Downstream Users Products That Work Better for Privileged People Dark Patterns Fair Products Checklist Concluding Remarks Chapter 12. Laws for Machine Learning Personal Data GDPR California Consumer Privacy Act Data Broker Laws Algorithmic Decision Making GDPR Proposed US Laws for Algorithms Security HIPAA FTC Guidance on Cybersecurity Tort Law Logical Processes Right to an Explanation Freedom of Information Laws Due Process Some Application-Specific Laws Biometrics Local Ordinances on Facial Recognition Chat Bots Concluding Remarks Index About the Author Colophon
