Toward Best Practices for Explainable B2B Machine Learning

TL;DR

This paper discusses best practices for developing explainable B2B machine learning systems, emphasizing the importance of organizational context and enabling domain-expert users to explain models to various stakeholders.

Contribution

It introduces a comprehensive perspective on explainability in B2B ML, highlighting the need to consider organizational and secondary audiences in design.

Findings

Built custom ML chatbots for recruitment demonstrating explainability.

Emphasized the role of domain experts in explaining ML models.

Highlighted organizational context as key to effective explainability.

Abstract

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.