Grub first, then ethics.
With the use of data comes the misuse of data. This has pretty much always been the case, but recently this idea has been reified as “data ethics” and has featured somewhat prominently in the news.
For instance, in the 2016 election, a company called Cambridge Analytica improperly accessed Facebook data and used that for political ad targeting.
In 2018, an autonomous car being tested by Uber struck and killed a pedestrian (there was a “safety driver” in the car, but apparently she was not paying attention at the time).
Algorithms are used to predict the risk that criminals will reoffend and to sentence them accordingly. Is this more or less fair than allowing judges to determine the same?
Some airlines assign families separate seats, forcing them to pay extra to sit together. Should a data scientist have stepped in to prevent this? (Many data scientists in the linked thread seem to believe so.)
“Data ethics” purports to provide answers to these questions, or at least a framework for wrestling with them. I’m not so arrogant as to tell you how to think about these things (and “these things” are changing quickly), so in this chapter we’ll just take a quick tour of some of the most relevant issues and (hopefully) inspire you to think about them further. (Alas, I am not a good enough philosopher to do ethics from scratch.)
Well, let’s start with “what is ethics?” ...