The Arc of the Data Scientific Universe
This paper clarifies the ethical and epistemic foundations for responsible data science for researchers and policymakers, offering an incremental theoretical contribution to the philosophy of science and data ethics.
This paper analyzes the normative assumptions underlying Sabina Leonelli's concept of responsible and sustainable data work, particularly in the context of COVID-19. It traces the evolution of social thinking on scientific norms from Robert K. Merton to the present, arguing for a new framework of situated universalism, methodological pluralism, strong objectivity, and unbounded communalism to guide future data science.
In this paper I explore the scaffolding of normative assumptions that supports Sabina Leonelli's implicit appeal to the values of epistemic integrity and the global public good that conjointly animate the ethos of responsible and sustainable data work in the context of COVID-19. Drawing primarily on the writings of sociologist Robert K. Merton, the thinkers of the Vienna Circle, and Charles Sanders Peirce, I make some of these assumptions explicit by telling a longer story about the evolution of social thinking about the normative structure of science from Merton's articulation of his well-known norms (those of universalism, communism, organized skepticism, and disinterestedness) to the present. I show that while Merton's norms and his intertwinement of these with the underlying mechanisms of democratic order provide us with an especially good starting point to explore and clarify the commitments and values of science, Leonelli's broader, more context-responsive, and more holistic vision of the epistemic integrity of data scientific understanding, and her discernment of the global and biospheric scope of its moral-practical reach, move beyond Merton's schema in ways that effectively draw upon important critiques. Stepping past Merton, I argue that a combination of situated universalism, methodological pluralism, strong objectivity, and unbounded communalism must guide the responsible and sustainable data work of the future.