Abstract

There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. However, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy are often missed. In this article, we present a framework for bringing together key data science practices with ethical topics. We encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which is rife with potentially morally charged decision points.

Introduction

Data Science is an interdisciplinary field that often includes ethics as a core element. Ethics courses are common requirements for many Data Science majors. However, there is less consensus on the best practices for teaching these ethical courses. Some focus on building students’ understanding of the ethical issues in data science through case studies, while others focus on the philosophical conceptions surrounding key ethical terms.

A challenge for educators is to stay proficient in the evolving field of data science without losing sight of the centuries-old ethical theories. In this article, we explore the pedagogies of existing data science ethics courses using a philosophical lens. We also provide insights into pertinent pedagogical questions, like: Which topics are most common within data science ethics classes? What ethical areas are most pertinent to a data science lifecycle, and at what stage of the lifecycle does each ethical area play a distinct role?

Motivation

In order to motivate the pedagogical connections between data science and ethics, we start by providing frameworks for both data science and ethics. Various frameworks exist for both data science and ethics, but their common objective is to guide the application of the disciplines in a larger, data-driven decision-making context.

What is Data Science?

Data Science is often focused on transforming data into data models. However, the field encompasses all the processes needed to answer questions with data, from “Problem Definition” to “Deployment and Use”. In turn, these processes involve various steps, including defining the problem, processing the raw data, creating a tabulated version of the data, and finally, generating the data model(s).

In conclusion, philosophy plays a significant role in creating a comprehensive data science ethics course. By aligning data science practices with philosophical concepts, educators can equip students to understand and navigate the ethical landscape of the data science field.