Fair CRISP-DM: Embedding Fairness in Machine Learning (ML) Development Life Cycle

Simple item page
dc.contributor.authorSingh, Vivek
dc.contributor.authorSingh, Anshuman
dc.contributor.authorJoshi, Kailash
dc.date.accessioned2021-12-24T17:30:38Z
dc.date.available2021-12-24T17:30:38Z
dc.date.issued2022-01-04
dc.description.abstractWith rapid adoption of machine learning (ML) technologies, the organizations are constantly exploring for efficient processes to develop such technologies. Cross-industry standard process for data mining (CRISP-DM) provides an industry and technology independent model for organizing ML projects’ development. However, the model lacks fairness concerns related to ML technologies. To address this important theoretical and practical gap in the literature, we propose a new model – Fair CRISP-DM which categorizes and presents the relevant fairness challenges in each phase of project development. We contribute to the literature on ML development and fairness. Specifically, ML researchers and practitioners can adopt our model to check and mitigate fairness concerns in each phase of ML project development.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.190
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79522
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFairness in Algorithmic Decision Making
dc.subjectcrisp-dm
dc.subjectfairness
dc.subjectmachine learning
dc.titleFair CRISP-DM: Embedding Fairness in Machine Learning (ML) Development Life Cycle
dc.type.dcmitext

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0151.pdf
Size:
274.51 KB
Format:
Adobe Portable Document Format
Download

Collections

Fairness in Algorithmic Decision Making