For this weeks Rose Café, we read the book “Automating Inequality” by Virginia Eubanks which is about how the government is automating social services using lots of data and predictive models to increase efficiency. Eubanks highlights three cases where automatization was actually detrimental to those people in poverty who relied on these services, and ultimately served to reinforce their position as poor and underserved Americans. It was really interesting to listen to lots of different perspectives on the issue, and to discuss some of the broader implications for increasing data collection on everybody. As a biology major, I particularly enjoyed listening to the perspectives from some of the computer and information science majors about data collecting, and the difficulties associated with designing complex systems and algorithms that are supposed to try to eliminate bias and increase accuracy and efficiency in welfare. Despite our continuous advances in technology though, a really interesting point was brought up that even if these systems are supposed to eliminate bias in how they award social services, the computer systems are still designed by humans and therefore are subject to implicit biases. Combined with a political agenda, and potential monetary incentives, it is no wonder that these attempts toward automation resulted in failure. A final point that also stuck with me was that these systems used to surveil and track the poor are also being used on middle and upper class citizens, which has made me think of all the ways that the “system” is influencing my life, and how a potential malfunction would directly affect me.