Grab hold of your cape, because we all have the potential to be superheroes now. This was the message from Hilary Mason during her talk ‘Data as a Technical Superpower’ at Barnard College, Wednesday 3rd December. During an engaging, insightful – and often playful – lecture, Ms. Mason covered a range of topics related to what has become known as “big data” and its applications in a variety of fields. Throughout the talk, these topics circled back to the main point that utilizing tools to explore the masses of data produced in today’s world can give us new insights into research problems and help us perceive things we might not otherwise. It may not be as flashy as X-ray vision but this is data as a superpower.
Although not explicitly about DH, this lecture revealed a lot of parallel approaches to data and concerns that were relevant and illuminating for DH researchers. To begin at the beginning, Ms. Mason placed today’s technology in its historical context, looking at one of the first big data problems: the 1880 US Census. This census took seven years to complete. By the time of the next census in 1890 the population had doubled, so a new system was needed to complete it within a ten year window. To solve this problem Herman Hollerith invented a tabulating machine that could read punch cards (his company would subsequently become IBM). From these humble beginnings, she then traced the development of technological approaches to data problems before arriving at the somewhat more advanced ‘counting machines’ in widespread use today. Ms. Mason was able to show that many research problems today are conceptually just extensions of these initial approaches (albeit with bigger data sets). Framing modern data problems in this way was extremely useful to help understand and theorize modern day approaches to big data.
Ms. Mason then guided the audience through a host of example projects and tools drawn from her own career at Bitly and Accel as well as further afield. These examples, often irreverent, illustrated how data can reframe questions and provide insights we wouldn’t otherwise reach. Through all of these examples, the human element was not dismissed. Tools do not tell us anything in and of themselves; instead, as Ms. Mason has it, the data is made useful for solving human problems. Here was the significance of the ‘superpower’ aspect of the title – the technology is an aid and extension of human behaviours such as perception and interpretation: whether this be the data scientists at the OKCupid blog using data gathered from their site to tell stories about dating or Forbes mapping the influence of media companies through assessing link clicks. This aligns closely with many theoretical approaches in DH. As Franco Moretti notes in Graphs, Maps, Trees, quantitative research “provides data, not interpretation.” (p. 9). For Scott Weingart, this opens up a niche for digital humanities in today’s data driven world: acting as a bridge between the macro and the micro. It is important that the distant readings of big data do not obscure the importance of the micro-level, human impact of this data, and this is an area that DH is ideally suited to address.
Rounding off the lecture was a thoughtful discussion about some of the challenges of working with big data, framed by Ms. Mason’s experiences as co-host of DataGotham and co-founder of hackNY. Some of these challenges, including the ethics of collecting and using data, as well as concerns regarding privacy and effective regulations, have been brought into wider consciousness by the revelations of Edward Snowden. Ms. Mason argues the need for a more robust discussion and response to these issues from the data community as a whole (incidentally, Ms. Mason’s Fast Forward Labs always includes a section on the ethical implications of algorithms in their white papers). Further, the challenges of addressing how humanity interacts with these systems and how they impact us and enrich us as humans are important considerations that provide a neat segue into digital humanities. For many in the field, digital humanities work is not just about using tools to compute data, but also must include a deeper reflection on these technologies and their impact, continuing the long tradition of critical humanistic enquiry. Alan Liu goes further than most in his article ‘Where Is Cultural Criticism in the Digital Humanities?’ arguing that this reflective questioning should take the form of wider cultural criticism and address digital humanities’ relationship to “postindustrial, neoliberal, corporate, and global flows of information-cum-capital.” In an online exchange via blogs and Twitter with Stephen Ramsay in response to this initial article, Liu clarifies his position, asserting;
“The digital humanities can only take on their full importance when they are seen to serve the larger humanities (and arts, with affiliated social sciences) in helping them maintain their ability to contribute to the making of the full wealth of society, where “wealth” here has its older, classic sense of “well-being” or the good life woven together with the life of good.” (Liu)
Whether or not you agree with Alan Liu, that digital humanities needs to tackle critical theory to enhance its relevance, there is a definite need to engage critically with projects for DH to reach its fullest expression. Although Ms. Mason was not discussing digital humanities projects specifically, the issues and challenges she was addressing were similar to those that have provoked debate within DH circles. Following from this, it could be argued that all big data research projects would benefit from adopting a humanist perspective to reflect critically on these tools, methods and how they impact society or shape our concept of society (even if that tool is just a script that generates random images of kittens). A collaboration between humanists, governments, industries and the public could enhance and enrich the results of any big data problem, allowing the researchers to become, in Weingart’s words, “ethical stewards” of this big data world. If the superheroes of many comic books are prone to moments of self reflection and questioning, it shouldn’t be too much to ask today’s data superheroes to do the same.
Thank you to Ms. Mason and the Empirical Reasoning Lab at Barnard College for such a stimulating, insightful and entertaining lecture.
Liu, A. (2012). “Where is cultural criticism in the Digital Humanities?” In M. Gold (Ed.), Debates in the Digital Humanities (490-509). Minneapolis: University of Minnesota Press.
Liu, A. (2013). ‘Why I’m In It’ x 2 – Antiphonal Response to Stephan Ramsay on Digital Humanities and Cultural Criticism. Retrieved from http://liu.english.ucsb.edu/why-im-in-it-x-2-antiphonal-response-to-stephan-ramsay-on-digital-humanities-and-cultural-criticism/
Moretti, F. (2007). Graphs, maps, trees: Abstract models for literary history. New York: Verso.
Weingart, S. (2014). The moral role of DH in a data-driven world. Retrieved from http://www.scottbot.net/HIAL/?p=40944