Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, and influential samples, among other techniques. Recent advances in this line of work, however, have gone without surveys of how organizations are using these techniques in practice. This study explores how organizations view and use explainability for stakeholder consumption. We find that the majority of deployments are not for end users affected by the model but for machine learning engineers, who use explainability to debug the model itself. There is a gap between explainability in practice and the goal of public transparency, since explanations primarily serve internal stakeholders rather than external ones. Our study synthesizes the limitations with current explainability techniques that hamper their use for end users. To facilitate end user interaction, we develop a framework for establishing clear goals for explainability, including a focus on normative desiderata.

本研究探讨企业机构如何使用可解释机器学习来提供给利益相关者洞察模型行为，发现目前解释主要面向机器学习工程师，而不是受到模型影响的最终用户，存在内外部利益相关者的差异。研究综合分析了当前解释技术的局限，提出了促进最终用户交互的可解释性目标框架，并讨论了相关的问题。

部署中的可解释机器学习