Integrating Fairness in Machine Learning Development Life Cycle: Fair CRISP-DM.

Developing efficient processes for building machine learning (ML) applications is an emerging topic for research. One of the well-known frameworks for organizing, developing, and deploying predictive machine learning models is cross-industry standard for data mining (CRISP-DM). However, the framework does not provide any guidelines for detecting and mitigating different types of fairness-related biases in the development of ML applications. The study of these biases is a relatively recent stream of research. To address this significant theoretical and practical gap, we propose a new framework—Fair CRISP-DM, which groups and maps these biases corresponding to each phase of an ML application development. Through this study, we contribute to the literature on ML development and fairness. We present recommendations to ML researchers on including fairness as part of the ML evaluation process. Further …