Mathematical Values in Data Science: Three conjectures to explain mathwashing

Activiteit: Talk or presentation at a conference


With the development of a critical research agenda on contemporary data practices we gradually build the tools that are needed to overcome the uncertainty, lack of clarity, and impact of misleading narratives concerning the epistemology of data science. Without such a reflection, we cannot understand the kind of knowledge data analysis produces. More importantly, we then also lack the ability to evaluate specific knowledge-claims as well as more general affirmations of the epistemic superiority (smarter, more objective, ...) of the knowledge, decisions, or insights that data analysis produces. This is why it is important to recognise that data is never just data (e.g. Gitelman 2013, Kitchin 2014), or that the development of algorithms (as any advanced scientific or engineering practice) cannot fully be understood in terms of a well-defined internal logic.

The starting point of this contribution is that we should start asking similar questions about mathematics: We need to understand how mathematics contributes to scientific respectability and authority of data science. To do so, we cannot limit our attention to mathematics as a body of mathematical truths or mathematical techniques. Instead, we should focus on mathematical thought and beliefs about the nature of mathematical thought. I propose to develop this critical inquiry through a dedicated consideration of how mathematical values shape data science.
Periode10 dec 201811 dec 2018
EvenementstitelData, Security, Values: Vocations and Visions of Data Analysis: Annual Conference of the Nordic Centre for Security Technology and Societal Values (NordSTEVA)
LocatieOslo, Norway
Mate van erkenningInternational