Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms.

There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.

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Spis treści

Practical Fairness eBook -- spis treści

• Preface
  • Goals of This Book
  • Practical Notes on the Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 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
      • Information collection
      • Information processing
      • Information dissemination
      • Invasion
      • A way of thinking about privacy: contextual integrity
  • Legal Responses to Fairness in Technology
  • The Assumptions and Approaches in This Book
  • What If Im Skeptical of All This Fairness Talk?
    • Wont Fairness Slow Down Innovation?
    • Are There Any Real-World Consequences for Not Developing Fairness-Aware Practices?
  • What Is Fairness?
  • Rules to Code By
• 2. Understanding Fairness and the Data Science Pipeline
  • Metrics for Fairness
    • Measures of Equity
      • Antidiscrimination measures of equity
      • Rationality measures of equity
    • Measures of Privacy
      • Technical measures of privacy protection
        • k-anonymity
        • Differential privacy
      • Human measures of privacy protection
    • 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
  • Concluding Remarks
• 3. Fair Data
  • Ensuring Data Integrity
    • True Measurements
      • Proxies
      • Failures of external validity
      • Undescribed variation
    • Proportionality and Sampling Technique
      • Biased sampling
      • Data duplication
  • Choosing Appropriate Data
    • Equity
      • Fair play
      • Reasonable expectations
    • Privacy
      • Personal data
      • Metadata collection
      • Proxies for private information
    • Security
      • Data collected from the wild is dangerous
      • Inherently dangerous data
      • Naively chosen data
      • Incomplete data
  • 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
• 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
• 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
• 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
• 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
      • How it works
      • Code demonstration
        • The data set
        • A vanilla audit with default library options
        • A black-box model audit
  • Concluding Remarks
• 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
      • LIME
        • How it works
        • Code example
      • SHAP
        • How it works
        • Code example
    • Data-Driven Explanation
      • How it works
      • Code example
    • Explainability Metrics
  • What Interpretation and Explainability Miss
    • Attacks on Explainable Machine Learning
  • Interpretation and Explanation Checklist
  • Concluding Remarks
• 9. ML Models and Privacy
  • Membership Attacks
    • How It Works
    • Code Demonstration
  • Other Privacy Problems and Attacks
  • Important Privacy Techniques
  • Concluding Remarks
• 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
• 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
• 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