Practice: Reflecting on When to Not Use ML

These questions are meant to be open-ended and there is no right answer. The intent here is to get you thinking about the “When to Not Use ML” slide and writing out your thoughts. Full credit will be awarded for answers that show an honest effort put into them.

Question 0

Did you find anything interesting, challenging, or thought-provoking in the reading “When to Not Use ML”? How does that add to your perspective of when to use ML?

Your Task

Write your answer down in your own space.

Question 1

Consider the case of our credit card churn predictor. Suppose we were using it in our first use case of predicting whether a current customer is likely to churn, and if they are, provide them with special offers to incentivize them to stay.

Would you endorse using such a system? Why or why not? Justify what concerns you might have about this system or why you think some potential concerns do not outweigh the benefit of the model.

You don’t need to answer every question that we posed on that slide, but you might find one or more of them useful in framing your thoughts.

Your Task

Write your answer down in your own space.

Question 2

Consider the case of our credit card churn predictor. Suppose we were using it in our second use case of predicting whether a new customer is likely to churn or not, and if they are, don’t provide them with a credit card in the first place.

Would you endorse using such a system? Why or why not? Justify what concerns you might have about this system or why you think some potential concerns do not outweigh the benefit of the model.

You don’t need to answer every question that we posed on that slide, but you might find one or more of them useful in framing your thoughts.

Your Task

Write your answer down in your own space.