Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects
This work addresses the challenge of understanding and representing factors in model selection for data science projects, with a focus on bias, but it appears incremental as it builds on existing variability-aware methods.
The paper tackles the problem of machine learning model selection by extending a variability-aware method to include bias detection, aiming to make selection factors explicit and transform it into a non ad hoc, adaptive, and explainable process, as illustrated in a heart failure prediction case study.
Data science projects often involve various machine learning (ML) methods that depend on data, code, and models. One of the key activities in these projects is the selection of a model or algorithm that is appropriate for the data analysis at hand. ML model selection depends on several factors, which include data-related attributes such as sample size, functional requirements such as the prediction algorithm type, and non-functional requirements such as performance and bias. However, the factors that influence such selection are often not well understood and explicitly represented. This paper describes ongoing work on extending an adaptive variability-aware model selection method with bias detection in ML projects. The method involves: (i) modeling the variability of the factors that affect model selection using feature models based on heuristics proposed in the literature; (ii) instantiating our variability model with added features related to bias (e.g., bias-related metrics); and (iii) conducting experiments that illustrate the method in a specific case study to illustrate our approach based on a heart failure prediction project. The proposed approach aims to advance the state of the art by making explicit factors that influence model selection, particularly those related to bias, as well as their interactions. The provided representations can transform model selection in ML projects into a non ad hoc, adaptive, and explainable process.