How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Abstract

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.

Time to Focus: A Comprehensive Benchmark using Time Series Attribution Methods

“…The overall results are presented in Table 8. Teh resuls highlight that choosing an attribution method can be very important, as mentioned by Vermeire et al (Vermeire et al, 2021).…”

Optimal Neighborhood Contexts in Explainable AI: An Explanandum-Based Evaluation

“…In [14], it is emphasised that CDSS should mimic the clinical decision-making process based on optimising workflows, especially in low-resource settings. While theoretical methodologies for evaluating XAI techniques are discussed in [39], [40], they have not demonstrated potential applicability using experimental evaluations.…”