How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
This addresses the challenge for organizations needing to implement explainable AI in practice, but it is incremental as it builds on existing work in human-computer interface and machine learning communities.
The paper tackles the problem of selecting appropriate explainability methods for automated decision-making by proposing a methodology to bridge the gap between stakeholder needs and explanation methods, with contributions including documents to characterize XAI methods and user requirements.
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.