论文标题
明智的AI：使用感官理论重新想象可解释性和解释性
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
论文作者
论文摘要
了解ML模型的工作方式是负责任地设计，部署和使用基于ML的系统的先决条件。使用可解释性方法，ML现在可以为其产出以帮助人类理解提供解释。尽管这些方法依赖于人类如何彼此解释的准则，但它们最终解决了改善工件的解释 - 一种解释。在本文中，我们为基于Weick的感官理论提出了一个基于解释性的替代框架，该框架着眼于解释的目的。最近的工作提倡理解利益相关者需求的重要性 - 我们通过提供具体的属性（例如身份，社会环境，环境提示等）来建立在此基础上。我们使用在组织中的感官的应用作为一种模板，用于讨论明智的AI设计指南，AI，在试图解释自我时会因人类认知的细微差别而产生。
Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact -- an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick's sensemaking theory, which focuses on who the explanation is intended for. Recent work has advocated for the importance of understanding stakeholders' needs -- we build on this by providing concrete properties (e.g., identity, social context, environmental cues, etc.) that shape human understanding. We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.
