# The Arc of the Data Scientific Universe

arXiv: https://arxiv.org/abs/2102.10050 · HTML: https://mlsanity.com/paper/2102.10050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 95 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research](https://mlsanity.com/paper/2010.15581/) (2020)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity](https://mlsanity.com/paper/2009.13676/) (2020)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [Artificial Intelligence for Social Good](https://mlsanity.com/paper/1901.05406/) (2019)
- [Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction](https://mlsanity.com/paper/2008.06755/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Critical Examination of the Ethics of AI-Mediated Peer Review](https://mlsanity.com/paper/2309.12356/) (2023)
- [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022)

## Abstract (arXiv)

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.