Today's software applications need more than a friendly interface and correct algorithms. They also need to be responsible: to be beneficial for society and not cause harm. In an era of AI chatbots, deep fake images and videos, social media bubbles, expanding privacy regulations, and a warming planet, it's more important than ever to practice responsible software engineering so your products earn your users' trust—and deserve it.

Responsible Software Engineering gathers the wisdom of over 100 Google employees to help you anticipate the effects of your software on the world and its inhabitants. It features expert advice and practical case studies so you can build better applications that are more ready for real-world situations:

• Treating people more fairly, regardless of their beliefs, culture, skin tone, and other attributes
• Operating more safely, to reduce the risk of physical, psychological, or financial harm
• Better protecting people's privacy, particularly when collecting personal information
• Incorporating wisdom from the social sciences, law, ethics, and other fields that many engineers may be unfamiliar with
• Reducing emissions of carbon dioxide (CO₂), to address the risks of climate change

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Spis treści

Responsible Software Engineering. With Real-World Case Studies from Google eBook -- spis treści

• Preface
  • Whats in This Book?
  • Whats Not in This Book?
  • A Note About the Characters
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Responsible Software Engineering: A Quick Introduction
  • What Is Responsible Software Engineering?
    • A Little Help from Some Specialists
    • What Is Responsible Engineering Not?
  • A Little History
  • Adopting a Responsible Mindset
  • Summary
• 2. Creating AI Systems That Work Well for Everyone
  • What Is Fairness?
  • Why Is Fairness Hard?
    • Fairness Is Different from Accuracy
    • Fairness Is Relative
    • Bias Is Always Present
    • AI Input Can Be Ambiguous
    • AI Output Can Be Hard to Evaluate
  • Evaluating Fairness
    • Parity Issues
      • Counterfactual fairness
      • Ungrounded inferences
    • Stereotyping Issues
    • Accuracy Issues
    • Combinations of Issues
    • Resources for Evaluating Fairness
  • Mitigating Fairness Issues, in Brief
    • People- and Process-Related Suggestions
    • Technology Solutions
      • Mitigating marked language
      • Using model cards
      • Red teaming
      • Performing scaled evaluations
  • Case Study: Oversexualized Generated Imagery
  • Summary
• 3. Incorporating Societal Context
  • What Is Societal Context?
    • Issues of Abstraction
    • Making Your Causal Assumptions Explicit
  • Mitigating Bias in the Care Management Algorithm
  • Best Practices
    • Identifying Agents, Artifacts, and Precepts
    • Creating a Welcoming Environment for Exchanging Viewpoints
      • Drawing on a wide variety of perspectives
      • Having a strong facilitator
      • Wearing the Six Thinking Hats
  • Case Study: Detecting Toxic Comments
  • Summary
• 4. Anticipating and Planning for Downstream Consequences
  • Safety and Harm
    • Types of Harm
    • Testing for Safety
    • How Is Safety Related to Ethics?
    • Common Justifications for Sidestepping Ethical Behavior
  • Methods for Anticipating Consequences
    • Testing with Breadth
    • Codesigning with Users
    • Reviewing a List of Harms
    • Practicing Future Regret
    • Running Tabletop Exercises
    • Implementing Abuser and Survivor Testing
    • Stress-Testing Your Applications
    • Trying Chaos Engineering
    • Educating Yourself About Other Peoples Lives
  • Case Study: Googles Moral Imagination Workshop
    • Preparations
      • Step 1: Choose a facilitator
      • Step 2: Obtain a list of values
      • Step 3: Gather the team
      • Step 4: Describe your project
      • Step 5: Choose the teams top values from the list
      • Step 6: Determine what the four values mean for the project
      • Step 7: Apply each value to the project
    • What Next?
      • Creating a scenario and having a role-playing session
      • Conducting harm exercises
      • Creating an action plan
  • Summary
• 5. Securing and Respecting Users Privacy
  • What Is Privacy?
    • Personally Identifiable Information
    • Data Collection, Trade-offs, and Convenience
  • Privacy from the Users Perspective
    • No Surprises
    • Transparency
    • Consent
    • Control
  • Privacy from a Data Perspective
    • Minimization
    • Retention
    • Anonymization
      • Generalization and obfuscation
      • k-anonymity
      • Differential privacy
  • From Tools to Policy
  • Case Study: Protecting Privacy During the COVID Pandemic
  • Living and Working in a Privacy-Focused World
  • Summary
• 6. Measuring and Reducing Your Codes Carbon Footprint
  • Measuring Carbon Emissions
    • Principles of Power
    • Beyond Direct Carbon Emissions
  • Controlling Your Codes Carbon Footprint
    • Controlling Processor Usage
    • What About Coding for Performance?
    • Controlling the Codes Location
    • Optimizing for Time of Day
  • Getting Involved
  • Case Study: Cooling a Data Center with AI
  • Summary
• 7. Building a Culture of Responsible Software Engineering
  • Setting Policy
    • Sponsorship and Support
    • Misunderstandings About a Culture of Responsibility
      • Its just branding.
      • Its just compliance.
      • Its just bureaucracy.
  • Spreading the Word
    • Messaging
    • Educating New Hires
  • Establishing Processes
  • Creating Incentives
    • Incentives from the Top Down
    • Incentives from the Bottom Up
  • Learning from Mistakes
  • Measuring Success
  • Case Study: The Responsible Innovation Challenge
  • Summary
• Index