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.

翻译：数据科学项目通常涉及多种依赖于数据、代码和模型的机器学习方法。这些项目的关键活动之一是选择适合当前数据分析的模型或算法。机器学习模型选择受多种因素影响，包括样本量等数据相关属性、预测算法类型等功能需求，以及性能和偏差等非功能需求。然而，影响此类选择的因素往往未被充分理解并明确表征。本文描述了在机器学习项目中扩展一种自适应可变性感知模型选择方法以融入偏差检测的进展。该方法包括：(i) 基于文献中提出的启发式策略，使用特征模型对影响模型选择的因素进行可变性建模；(ii) 通过添加与偏差相关的特征（如偏差相关指标）来实例化我们的可变性模型；(iii) 在特定案例研究中进行实验，以基于心力衰竭预测项目阐述我们的方法。本方法旨在通过明确影响模型选择的因素（尤其是与偏差相关的因素）及其相互作用，推动该领域的发展。所提供的表征可将机器学习项目中的模型选择转变为非临时性、自适应且可解释的过程。