Asher Brandon Caplan

This paper identifies technological practice—practice which is social yet non-propositional—as a central feature of pragmatist accounts of normativity. Following past efforts to bridge the opposing attitudes and methodologies of John Dewey and Ludwig Wittgenstein through their shared use of language-as-tool metaphors, I argue that resources from the philosophy of technology can be drawn upon to examine these metaphors in a new light. I demonstrate that both Dewey and Wittgenstein mobilize complementary tools-as-language metaphors, which extend meaning holism to the non-linguistic material objects of our social practices. The resulting position reconceptualizes and problematizes past debates within pragmatist scholarship by articulating a communicative-theoretic pragmatism which locates and tracks the norms implicit in our use of tools, instruments, and artifacts.

Data interoperability poses unique ethical challenges across a range of academic, industrial, and governmental implementations of data systems. Central to data interoperability is the design of systems and protocols for exchanging or integrating data from different initial source domains. Data interoperability is often regarded as necessary for carrying out tasks between different organizations and suborganizations as well as for ensuring secondary use of data for research purposes. However, interoperability poses a number of ethical problems whose contours can prove especially challenging in comparison to how ethical harms take hold at other moments of the data life cycle (such as algorithmic processing or results dissemination). Taking biomedical data interoperability as a focal domain, this article provides an overview of data interoperability, maps the central ethical harms that may challenge interoperability projects, and proposes a response to these problems through an approach rooted in philosophical pragmatism. Pragmatist responses to both individual and structural harms of interoperability are presented through three companion strategies: shared standards, manual data curation, and meticulous data documentation.