Key concepts for a data science ethics curriculum

Jeffrey Saltz, Neil I. Dewar, Robert Heckman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Data science is a new field that integrates aspects of computer science, statistics and information management. As a new field, ethical issues a data scientist may encounter have received little attention to date, and ethics training within a data science curriculum has received even less attention. To address this gap, this article explores the different codes of conduct and ethics frameworks related to data science. We compare this analysis with the results of a systematic literature review focusing on ethics in data science. Our analysis identified twelve key ethics areas that should be included within a data science ethics curriculum. Our research notes that none of the existing codes or frameworks covers all of the identified themes. Data science educators and program coordinators can use our results as a way to identify key ethical concepts that can be introduced within a data science program.

Original languageEnglish (US)
Title of host publicationSIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery, Inc
Pages952-957
Number of pages6
Volume2018-January
ISBN (Electronic)9781450351034
DOIs
StatePublished - Feb 21 2018
Event49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018 - Baltimore, United States
Duration: Feb 21 2018Feb 24 2018

Other

Other49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018
CountryUnited States
CityBaltimore
Period2/21/182/24/18

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Keywords

  • Big data
  • Computing education
  • Data science
  • Ethics

ASJC Scopus subject areas

  • Computer Science(all)
  • Education

Cite this

Saltz, J., Dewar, N. I., & Heckman, R. (2018). Key concepts for a data science ethics curriculum. In SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education (Vol. 2018-January, pp. 952-957). Association for Computing Machinery, Inc. https://doi.org/10.1145/3159450.3159483