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

Date
2022-01-04
Authors
Singh, Vivek
Singh, Anshuman
Joshi, Kailash
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Abstract
With 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.
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Keywords
Fairness in Algorithmic Decision Making, crisp-dm, fairness, machine learning
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