走向可解释的B2B机器学习的最佳实践
Toward Best Practices for Explainable B2B Machine Learning
摘要：
要为可解释的B2B机器学习（ML）系统设计工具和数据管道，我们不仅需要认识到这些工具和数据的直接受众，还要认识到（1）它们的组织背景和（2）次要受众。我们的学习是基于为招聘构建基于ML的自定义聊天机器人。我们认为，在B2B背景下，“可解释的”ML不仅意味着一个可以通过工具和数据管道“解释自己”的系统，而且还使其领域专家用户能够向其他利益相关者解释它。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
