Data Ethics: Making Data-Driven Decisions

Data Ethics: Making Data-Driven Decisions

Most companies have complex computer algorithms deciding who gets a bank loan, job interview, or health insurance. Do you have an ethical obligation to explain the decision-making back to your customer? How do you design systems that are free of gender or racial bias? These systems will define your organization. Yet many of these decisions aren’t happening in the boardroom. Instead they’re made in much smaller meetings with people just like you—project managers, business analysts, directors, and software developers. This course gives you the skills you need to make the best decisions. Instructor Doug Rose helps you consider the duties you have to your customer, think about the consequences of your algorithms’ decisions, and acting virtuously when wrestling with key data ethics challenges.

This course was created by Doug Rose. We are pleased to offer this training in our library.

Topics include:

  • Define ethics and distinguish between virtue ethics, utilitarianism, and objectivism.
  • Define and compare the categorical imperatives.
  • Describe what it means for an algorithm to be traceable.
  • Compare situations in which data should be accessible and inaccessible.
  • Define data bias and describe how it can arise.
  • Describe approaches to combat data bias and achieve fairness.


  • 英文名称:Data Ethics: Making Data-Driven Decisions
  • 时长:1小时5分
  • 字幕:英语


  1. Ethical decision-making
  2. Being a moral company
  3. How to approach company
  4. Start with ethical objectivism
  5. Think about your categorical imperatives
  6. What would a virtuous person do?
  7. The seven major data ethics challenges
  8. The right to algorithmic
  9. Data accessibility and comprehensibility
  10. Can anyone access their data?
  11. Trace your black box decisions
  12. Open the box with Explainable AI (XAI)
  13. Self-driving cars' trolley problem
  14. Decide how to crash a self-driving car
  15. What does data objectivity mean?
  16. Ways to think about bias
  17. How to fix data bias
  18. Can data be objective?
  19. What is fairness?
  20. Next steps