One of the most frustrating aspects of an ethics curriculum is that it always trails innovative computing developments; helpfully, the authors address that critical problem here. The authors argue that “data science is a new field that [combines] computer science, statistics, and information management.” As a result of its novelty and inter-disciplinary nature, ethical issues have been neglected; more importantly, a data science ethics curriculum has received little attention. To address this oversight, the authors engaged in a systemic literature review to identify “12 key ethics areas that they maintain should be included [in] a data science ethics curriculum.”

Of those key areas, one of the most critical components that a data science ethics curriculum must address is in the value chain of big data. Big data is a subset of data science and yet existing codes, such as the ACM Code of Conduct or the Data Science Association Code of Conduct, may not sufficiently address relevant issues.

The ubiquitous nature of data is obvious, but the ethical manner of using such data has not been clear. In existing analytical models, data can subjectively be used in terms of which algorithm to use, which data sources to use, what data point can be a proxy for an unknown fact, or how to interpret results. Ethical clarification is required particularly now since there is a public perception that data misuse impinges on privacy.

The value of the work identifies the gap in ethics education within existing data science education.