Abstract
The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses reveal that students tend to neglect ethical concerns in their data analysis. Based on the data we collected from questionnaires distributed to undergraduate science and engineering students, this paper expands the discussion beyond human concerns and ethics in data science education. As we will show, students tend to neglect the context when attempting to solve data science questions. We argue that gaps in understanding the context relating to the data result in gaps in the analysis as well as in the interpretation of the data. Thus, we propose anthropological thinking as a pedagogy to overcome the context neglect. Placing the spotlight on the context promotes a holistic understanding of the phenomenon being analyzed, as it includes important considerations that do not necessarily fit the more commonly used term human concerns.
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Notes
For more on Harvard’s Embedded Ethics pedagogy, see https://embeddedethics.seas.harvard.edu/.
During the first two weeks of the semester, students can make schedule changes. At the beginning of the third week, 49 students of the original 55 students remained and continued to study the course for the remainder of the semester.
The quote is attributed to Rabbi Shemuel ben Nachmani, as quoted in the Talmudic tractate Berakhot (55b.) (https://quoteinvestigator.com/2014/03/09/as-we-are/).
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Binah-Pollak, A., Hazzan, O., Mike, K. et al. Anthropological thinking in data science education: Thinking within context. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-023-12444-7
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DOI: https://doi.org/10.1007/s10639-023-12444-7