Toward Best Practices for Explainable B2B Machine Learning
This work addresses the challenge of making ML systems interpretable for business stakeholders, but it appears incremental as it builds on existing explainability concepts without introducing new methods.
The paper tackles the problem of designing explainable machine learning systems for B2B contexts by emphasizing the need to consider organizational context and secondary audiences, based on experiences from building custom ML-based chatbots for recruitment.
To design tools and data pipelines for explainable B2B machine learning (ML) systems, we need to recognize not only the immediate audience of such tools and data, but also (1) their organizational context and (2) secondary audiences. Our learnings are based on building custom ML-based chatbots for recruitment. We believe that in the B2B context, "explainable" ML means not only a system that can "explain itself" through tools and data pipelines, but also enables its domain-expert users to explain it to other stakeholders.