Machine Learning Explainability for External Stakeholders

TL;DR

This paper discusses the importance of explainability in machine learning for high-stakes applications, highlighting stakeholder needs, sharing case studies, and identifying challenges in deploying explainable models at scale.

Contribution

It presents insights from a multidisciplinary workshop on developing a shared language and understanding of explainability challenges and solutions in machine learning.

Findings

Shared language for explainability was developed

Case studies reveal practical deployment challenges

Open challenges in scaling explainable ML identified

Abstract

As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable. Providing useful explanations requires careful consideration of the needs of stakeholders, including end-users, regulators, and domain experts. Despite this need, little work has been done to facilitate inter-stakeholder conversation around explainable machine learning. To help address this gap, we conducted a closed-door, day-long workshop between academics, industry experts, legal scholars, and policymakers to develop a shared language around explainability and to understand the current shortcomings of and potential solutions for deploying explainable machine learning in service of transparency goals. We also asked participants to share case studies in deploying explainable machine learning at scale. In this paper, we provide a short summary of various case studies of explainable machine learning, lessons from those studies, and discuss open challenges.