Abstract / Synopsis
In this paper, we utilize duoethnography - a research method in which practitioners discursively interrogate the relationships between culture, context, and the mechanisms which shape individual autobiographical experiences - to explore what may be beyond ethics in the context of data science. Although ethical frameworks have the ability to reflect cultural priorities, a singular view of ethics, as we explore, often fails to speak to the multiple and diverse priorities held both within and across institutional spaces. To that end, this paper explores multiple perspectives, epistemologies, and worldviews that can collectively push researchers towards considerations of a data science education that is equity-minded both in concept and practice. Through a set of dialogues which examine our positionalities, journeys, ethics, local cultures, and accountabilities, this paper explores the contextual realities rooted in the authors’ educational settings. These conversations focus on the humanity of our students, the communities from which we come from and serve, as well as the unintentional harms and possibilities associated with the development of data science programs across institutional types. We take a set of five core questions to examine how we made, and continue to make, sense of our diverse cultural perspectives on data science education and equity with/in relation to others’ realities. Broadly, this paper seeks to offer reflections on the related but differing functions of ethics and equity in data science education.
Nathan Alexander, Carrie Diaz Eaton, Anelise H. Shrout, Belin Tsinnajinnie & Krystal Tsosie, "Beyond Ethics: Considerations for Centering Equity-Minded Data Science," Journal of Humanistic Mathematics, Volume 12 Issue 2 (July 2022), pages 254-300. DOI: 10.5642/jhummath.OCYS6929. Available at: https://scholarship.claremont.edu/jhm/vol12/iss2/14