Discovering Explainability Requirements in ML-Based Software

As the demand for Machine Learning (ML)-based software continues to grow across various industries such as healthcare, automotive, energy, and banking, there is an increasing need for explainability requirements. Domain experts such as doctors must have confidence in ML-based software to integrate them into their professional practices. This requires developers to simultaneously develop clear explanations of how these Machine Learning models work as they build the systems. While numerous philosophies and techniques for eliciting user requirements in software systems have been extensively studied within Requirements Engineering (RE), scholars argue that we need new approaches tailored to elicit explainability requirements. This PhD research aims to conduct empirical studies examining emerging methodologies and philosophies for identifying explainability requirements. The objective is to connect theoretical insights and practical approaches adopted by practitioners in this rapidly evolving field.