Digital Data 2019, Week 02

Week 02: Big Data Rewards and Risks

20-30% of road fatalities are due to driver fatigue.
According to the Transport Accident Commission, approximately 20% of road fatalities in Victoria, Australia are due to driver fatigue.

Researchers from many disciplines are excited about the opportunities that big data does, or perhaps will, make possible. Kenneth Cukier (2014), a journalist, identifies big data as a tool for societal advancement. We could, he suggests, treat posture as a unique identifier. With enough data, we could determine what changes in posture signal an imminent car crash due to driver fatigue and program cars to respond by taking actions designed to awaken and alert the driver. A panel of experts (SAGE, 2015) opines that big data will allow disciplines to ask questions collaboratively they wouldn’t be able to independently, a sort of intellectual intersectionality created by combining methods from computer science with hypotheses from sociology.

Others take a more nuanced perspective, recognizing both the opportunities and the challenges presented by big data. Susan Etlinger (2014) reminds us that “facts are stubborn things, but sometimes they’re stupid, too.” While recognizing how important standardization is for the semantic web, Susan Halford (2015) reminds us that naming is a complicated process (“What do you mean by London?”). Cory Doctorow pushed back against the very notion of standardization, “argu[ing] that in some cases a single representation [of each important concept] might not be appropriate, desirable, or fair” (Target, 2018). This concern certainly rings true for my own life and experience. A naming structure must be standardized and consistent to be helpful, but who gets to determine the standard? Transgender people, Black people, and women who have been married and divorced can all offer perspectives on the complicated and sometimes violent nature of naming in our society, but these are often not the people with a voice in such discussions.

When you work for a company or an institution that collects or trades data, you’re making it easy to surveil people and the stakes are high. They’re always high for the most vulnerable. By collecting so much data, you’re making it easy to discipline people. You’re making it easy to control people. You’re putting people at risk.

(Watters, 2017)

Those who are excited for the opportunities big data represents can be expected to downplay the associated risks with big data, but even those who do raise ethical or privacy concerns will not necessarily change their behavior. The American Sociological Association (2018) provides cover for sociologists on this front, stating that researchers in the discipline “do not typically need informed consent when using information from public internet sites” (p. 13) This ethical standpoint ignores the differences between being in public and being public (boyd & Crawford, 2012, p. 673) but is not unexpected from those for whom frequent, new, and groundbreaking research is highly incentivized. Likely few in this position would look at the ethical dilemmas and decide that they are too great to move forward at all.

This is not necessarily a criticism of how sociologists respond to professional pressures, but of the structures which create those pressures in the first place. These have always existed, but the difference now is that big data invisibly but systematically divides us into ever-finer slices of ourselves, making the human costs feel more remote against the increasingly necessary rewards. This, then, is the real challenge posed for sociology by big data: how do we weigh the potential individual and societal reward of big data research against the more distant-feeling individual and societal risk?

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American Sociological Association. (2018). ASA Code of Ethics. Retrieved from http://www.asanet.org/code-ethics

boyd, danah, & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Cukier, K. (2014). Kenneth Cukier: Big data is better data. Berlin. Retrieved from https://www.ted.com/talks/kenneth_cukier_big_data_is_better_data

Etlinger, S. (2014). What do we do with all this big data? Retrieved from https://www.ted.com/talks/susan_etlinger_what_do_we_do_with_all_this_big_data

Halford, S. (2015). Big Data & Digital Futures: Sociology Prize Winner’s Event @ the BSA Annual Conference 2015 – Part 1. Retrieved from https://www.youtube.com/watch?v=1xbwg2Y_u_k

SAGE. (2015). Digital Futures and Big Data: Sociology Vodcast number 9. Retrieved from https://www.youtube.com/watch?v=zdvhZNRbUdg

Target, S. (2018, May 27). Whatever Happened to the Semantic Web? Retrieved from https://twobithistory.org/2018/05/27/semantic-web.html

Watters, A. (2017, February 2). Ed-Tech in a Time of Trump. Retrieved from http://hackeducation.com/2017/02/02/ed-tech-and-trump

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