The Risk Management Process for Data Science: Gaps in Current Practices

Sucheta Lahiri, Jeffrey S. Saltz

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations

Abstract

Data science projects have unique risks, such as potential bias in predictive models, that can negatively impact the organization deploying the models as well as the people using the deployed models. With the increasing use of data science across a range of domains, the need to understand and manage data science project risk is increasing. Hence, this research leverages qualitative research to help understand the current practices concerning the risk management processes organizations currently use to identify and mitigate data science project risk. Specifically, this research reports on 16 semi-structured interviews, which were conducted across a diverse set of public and private organizations. The interviews identified a gap in current risk management processes, in that most organizations do not fully understand, nor manage, data science project risk. Furthermore, this research notes the need for a risk management framework that specifically addresses data science project risks.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages1198-1207
Number of pages10
ISBN (Electronic)9780998133157
StatePublished - 2022
Event55th Annual Hawaii International Conference on System Sciences, HICSS 2022 - Virtual, Online, United States
Duration: Jan 3 2022Jan 7 2022

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2022-January
ISSN (Print)1530-1605

Conference

Conference55th Annual Hawaii International Conference on System Sciences, HICSS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period1/3/221/7/22

ASJC Scopus subject areas

  • General Engineering

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