The Arc of the Data Scientific Universe

TL;DR

This paper examines the normative foundations of responsible data science, tracing the evolution from Merton's norms to a more holistic, context-sensitive framework emphasizing universalism, pluralism, and communalism for sustainable data practices.

Contribution

It explicitly connects sociological theories of scientific norms with contemporary ethical frameworks, proposing a nuanced, multi-faceted approach to responsible data work beyond traditional schemas.

Findings

Merton's norms provide a foundational understanding of scientific ethics.

Leonelli's broader vision incorporates context and global considerations.

A combination of universalism, pluralism, and communalism guides future responsible data practices.

Abstract

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.