In the first article, Hamish Robertson and Joanne Travaglia talk about how the first explosion of data in the 19th century was used in ways to categorize people and motivate “social change”. Whether that change was good change became irrelevant once certain assumptions, including negative social categories were built into the data collection process. Often this was used to oppress various categories. In this article, the authors express a concern that this same situation will be carried over to the “big data” revolution of the 21st century. Lev Manovich suggests that similar to the field of social science, computer scientists exploring social media use probabilistic models to analyze big data. Ted Striphas explores how common cultural words have changed due to the use of computing to produce, hold, and analyze cultural data.
I think the articles raise an interesting question since there is so much data now through social media and other online systems where people supply information that the criteria for categorizing people will be so much richer in the new data era. Furthermore, with the application of algorithms to data, errors, for example from an algorithm taking a string of information out of context, will be extremely likely and it is concerning what kind of influence such “mistakes” could have on our understanding of people and society. To what extent will small decisions in the way algorithms are designed and used impact society in ways we don’t understand? Since most of the decisions made by algorithms are probabilistic and our concepts of society are influenced by these decisions, how will we ensure that we are not causing societal damage by relying on the decisions of algorithms? These are especially issues because the large scale of big data magnifies a small decision made early on.