Our discussion of the book “Automating Inequality” by Virginia Eubanks was of particular interest to me as a computer science major since my future could find me implementing systems of similar importance and function. We discussed ways in which the collection and use of data for the sake of decision making has been detrimental to many people, especially those of lower classes, and possible solutions to the issue.

Of course, anybody who pays attention to sidebar or banner ads is probably familiar with the prevalence of data collection; many of us were able to think of situations in which we were given advertisements that strangely coincided with what we recently said or searched while unaware of being monitored. Thankfully, aside from being a bit creepy, this example of using data to make a decision (what advertisement to show an individual) doesn’t have very deep ramifications to the average consumer. However, when algorithms are designed to allocate money or other resources to those who need it, the topic turns much more serious. The technology being used in these situations is often flawed, either causing unnecessary difficulties to arise or demonstrating the biases of its creators in its function. While the idea of taking the decision making process out of the hands of humans, with all of our biases, emotions, and whatnot, the reality of the situation is that many of these new systems designed to help the lower classes are widening the gap between them and the upper classes.

Most of us in attendance agreed that some new legislation would be beneficial in such systems. However, we struggled in determining where to draw the line with data collection and analysis. I walked out of this discussion with more questions than answers, but I’m glad to be more informed about the issue than I was.