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As machine learning is increasingly deployed in high-stakes contextsaffecting people's livelihoods, there have been growing calls to open the blackbox and to make machine learning algorithms more explainable. Providing usefulexplanations requires careful consideration of the needs of stakeholders,including end-users, regulators, and domain experts. Despite this need, littlework has been done to facilitate inter-stakeholder conversation aroundexplainable machine learning. To help address this gap, we conducted aclosed-door, day-long workshop between academics, industry experts, legalscholars, and policymakers to develop a shared language around explainabilityand to understand the current shortcomings of and potential solutions fordeploying explainable machine learning in service of transparency goals. Wealso asked participants to share case studies in deploying explainable machinelearning at scale. In this paper, we provide a short summary of various casestudies of explainable machine learning, lessons from those studies, anddiscuss open challenges.

Machine Learning Explainability for External Stakeholders