CFP: Special Issue of Philosophy & Technology on The Epistemology of Data Science
Submission deadline: December 1, 2019
Call for Papers for a special issue of Philosophy and Technology (Springer) on: The Epistemology of Data Science
Data science is increasingly a force for innovation and potentially seen as a normative model for scientific research. Computational research-methods as well as efficient, smart, and potentially disruptive uses of data are quickly growing in popularity and significance, increasingly influencing all forms of knowledge-work. These developments rely on, but also reinforce a powerful narrative about the future of knowledge production. Traditionally, data science is characterised as a data- and computing-intensive techno-scientific practice, built on top of a heterogeneous array of methods for gathering, filtering, modifying, and analysing data using computational and statistical tools to extract information. This situates data science within a network of technological affordances, where more data, increasing computing power, and better mathematical tools enable new methods for extracting value from large data sets. However, data science can also be situated within a network of epistemic norms, values, practices, and epistemic virtues that are equally novel and revolutionary, and jointly constitute a “data science model of knowledge”. Finally, data science may also be seen as a specific style of analysis that inherits its epistemic criteria from the statistical modeling of regularities, is deployed in a social and technological context that enables its wider application, and whose use is validated and reinforced by a belief in its epistemic and/or pragmatic superiority. This overall perspective emphasises that data science may be not only a new way of doing science, but also a new model for how science could be done and how scientific practices and knowledge-work may evolve.
Given the importance and complexity of data science and its current impact on the development of knowledge, it is crucial to understand its nature, scope, and theoretical foundations. This is the goal of this special issue, which seeks to contribute and advance the current debate on the epistemology of data science by bringing together experts from a variety of scientific fields and scholarly traditions, including computer science, data science, epistemology, statistics, philosophy of computer science, philosophy of information, and philosophy of science, to analyse and ground the status of data science as a science.