Machine Learning Explainability for External Stakeholders
This addresses the problem of deploying explainable machine learning for transparency in high-stakes contexts, but it is incremental as it synthesizes existing case studies without introducing new methods.
The paper tackled the lack of inter-stakeholder conversation in explainable machine learning by conducting a workshop with academics, industry experts, legal scholars, and policymakers to develop a shared language and understand shortcomings and solutions, resulting in a summary of case studies, lessons, and open challenges.
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.