向谁解释？基于角色的可解释机器学习系统分析模型
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
摘要：
一些研究人员认为，机器学习系统的可解释性应该根据特定的代理或任务来定义：我们不应该问系统是否可解释，而是问它对谁可解释。我们描述了一个模型，旨在帮助回答这个问题，通过识别不同的角色，代理可以履行有关的机器学习系统。我们说明了我们的模型在各种情况下的使用，探索代理的角色如何影响其目标，以及定义可解释性的影响。最后，我们提出了建议，我们的模型可以是有用的解释性研究人员，系统开发人员和监管机构审计机器学习系统。
英文摘要：
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.
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。
