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Week 3: Ethics & Bias

1 min read (166 words)
#AI ethics  #bias  #fairness 

Week 3: Ethics & Bias#

Focus: Algorithmic bias and fairness

📚 Required Readings#

Primary Readings#

  1. “Weapons of Math Destruction” — Chapter 1 (30 min)

    • How algorithms can amplify inequality
    • Case studies of biased AI systems
    • The impact on vulnerable communities

  2. “Defining and Detecting Algorithmic Bias” (25 min)

    • Statistical definitions of fairness
    • Types of bias: historical, measurement, representation, evaluation
    • Methods for detecting and mitigating bias

Supplementary Resources#

  • ProPublica: “Machine Bias” investigation — Interactive article
  • “The Ethics of Artificial Intelligence” — Stanford Encyclopedia excerpt

💭 Discussion Prompts#

  1. How can an AI system be mathematically “accurate” yet fundamentally unfair?
  2. When is some level of bias acceptable in AI systems?
  3. Who should be responsible for the harms caused by biased algorithms?

📝 Preparation for Class#

  • Read about a real-world case of algorithmic bias (e.g., Amazon hiring, COMPAS, facial recognition)
  • Prepare a 2-minute summary: What went wrong? Who was harmed?
  • Brainstorm: How could that system have been designed more fairly?

🔗 Related Assignment#

See Week 3 Assignment for this week’s task.