Making Data Right: Embedding Ethics and Data Management in Data Science Instruction

Introduction And Background

Ethics are a critical part of data science education, as analytical and algorithmic decisions can have impactful consequences, both helpful and potentially harmful. This chapter describes approaches to embedding data ethics within data management education, and presents four case studies that demonstrate how academic libraries can successfully collaborate with partners on campus to incorporate these topics into data science instruction.

Interest and enrollment in data science programs in the United States have grown at a rapid rate over the last decade (Tang & Sae-Lim, 2016) to meet the rise in employer demand for data-specific technical and analytical skills. Many campuses that offer data science courses or programs share faculty in several different departments such as math, statistics, computer science, and business. This is true in the authors’ own institution, which offers a Data Science program in the College of Science and Engineering, but draws on faculty from departments across the entire university. Similarly, the “doing” of data science is not restricted to data science programs. According to Schutt and O’Neil (2013), a data scientist is anyone “who works with large amounts of data, and must grapple with computational problems posed by the structure, size, messiness, and the complexity and nature of the data” (p. 15). The work of data science is interdisciplinary and happens across departments and programs, as the datasets and methods used to make sense of them grow larger and more complex. As supporters of research across campus, academic libraries and research support offices are well positioned to reach and educate developing data scientists wherever they are. Many in these offices already perform a range of duties that can be categorized as data science support: managing research data repositories, leading workshops and classroom lectures, and research consulting.

Robinson and Nolis (2020) define data science as the “practice of using data to try to understand and solve real-world problems” (ch. 1.1 para. 1). To solve real-world problems with integrity, it is essential to recognize and heed ethical considerations about the collection, use, and sharing of data. These practices, as well as the organization of data throughout the research lifecycle, encompass what the authors refer to as data management. Data management is the decision-making process for how research data will be collected, organized, preserved, and shared. It is crucial that data scientists, and anyone doing data science work, not only understand what ethical issues might arise during the research process, but also know how to prevent them. For example, when data science teams incorporate representatives from the participant community into data collection, data analysis, and data preservation (and data destruction), the team is able to mitigate risks they may not have thought of on their own and also allows the community to have agency in this work (Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2019).