AutoML Insights: Gaining Confidence to Operationalize Predictive Models

Abstract

Automated machine learning (AutoML) tools empower organizations to unlock valuable new business insights, integrate advanced AI capabilities into applications, and enable both data scientists and non-technical experts to swiftly build predictive models. Complex models generated by AutoML can appear to operate as “black boxes.” This lack of transparency can be a major disadvantage in relation to compliance with the legislation (e.g., European Union regulations implementing a “right to explanation” of algorithmic decisions provided by artificial intelligence systems). Furthermore, in many applications one must not trust a black-box system by default. We approach the problem of confidence in models generated using AutoML frameworks in two areas: model explanation and model validation, respectively. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we propose a model-agnostic approach based on SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. We conducted a comparative analysis of three AutoML frameworks, examining their architectures and practical aspects, which aims at transparency in the generation of automatic learning models. Transparent model generation helps stakeholders understand how models are created, leading to greater confidence in their reliability and suitability for deployment in real-world scenarios.