Questionnaire

  1. Does ethics provide a list of “right answers”?
  2. How can working with people of different backgrounds help when considering ethical questions?
  3. What was the role of IBM in Nazi Germany? Why did the company participate as it did? Why did the workers participate?
  4. What was the role of the first person jailed in the Volkswagen diesel scandal?
  5. What was the problem with a database of suspected gang members maintained by California law enforcement officials?
  6. Why did YouTube’s recommendation algorithm recommend videos of partially clothed children to pedophiles, even though no employee at Google had programmed this feature?
  7. What are the problems with the centrality of metrics?
  8. Why did Meetup.com not include gender in its recommendation system for tech meetups?
  9. What are the six types of bias in machine learning, according to Suresh and Guttag?
  10. Give two examples of historical race bias in the US.
  11. Where are most images in ImageNet from?
  12. In the paper “Does Machine Learning Automate Moral Hazard and Error” why is sinusitis found to be predictive of a stroke?
  13. What is representation bias?
  14. How are machines and people different, in terms of their use for making decisions?
  15. Is disinformation the same as “fake news”?
  16. Why is disinformation through auto-generated text a particularly significant issue?
  17. What are the five ethical lenses described by the Markkula Center?
  18. Where is policy an appropriate tool for addressing data ethics issues?

Further Research:

  1. Read the article “What Happens When an Algorithm Cuts Your Healthcare”. How could problems like this be avoided in the future?
  2. Research to find out more about YouTube’s recommendation system and its societal impacts. Do you think recommendation systems must always have feedback loops with negative results? What approaches could Google take to avoid them? What about the government?
  3. Read the paper “Discrimination in Online Ad Delivery”. Do you think Google should be considered responsible for what happened to Dr. Sweeney? What would be an appropriate response?
  4. How can a cross-disciplinary team help avoid negative consequences?
  5. Read the paper “Does Machine Learning Automate Moral Hazard and Error”. What actions do you think should be taken to deal with the issues identified in this paper?
  6. Read the article “How Will We Prevent AI-Based Forgery?” Do you think Etzioni’s proposed approach could work? Why?
  7. Complete the section “Analyze a Project You Are Working On” in this chapter.
  8. Consider whether your team could be more diverse. If so, what approaches might help?