Abstract
As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML. However, existing explainability techniques often require dedicated backend servers, which limit their usefulness as the Web community moves toward in-browser ML for lower latency and greater privacy. To address the pressing need for a client-side explainability solution, we present WebSHAP, the first in-browser tool that adapts the state-of-the-art model-agnostic explainability technique SHAP to the Web environment. Our open-source tool is developed with modern Web technologies such as WebGL that leverage client-side hardware capabilities and make it easy to integrate into existing Web ML applications. We demonstrate WebSHAP in a usage scenario of explaining ML-based loan approval decisions to loan applicants. Reflecting on our work, we discuss the opportunities and challenges for future research on transparent Web ML. WebSHAP is available at this https URL.
Abstract (translated)
机器学习(ML)越来越融入到我们的日常生活中的Web体验中,因此需要有透明和可解释的Web机器学习的需求。然而,现有的可解释性技术通常需要专门的后端服务器,随着Web社区越来越倾向于在浏览器中实现机器学习以减少延迟和提高隐私性,这些技术的实用性受到限制。为了解决client-side可解释性解决方案的紧迫需求,我们提出了WebSHAP,它是第一个在浏览器中实现的可解释性工具,采用了最先进的模型无关可解释性技术SHAP。我们的开源工具采用了现代Web技术,例如WebGL,利用client-side硬件能力,使其很容易集成到现有的Web机器学习应用程序中。我们使用了WebSHAP来演示一个解释基于机器学习的贷款批准决策的使用场景。回顾我们的工作,我们讨论了透明Web机器学习的未来研究所面临的机会和挑战。WebSHAP可以在这个https://www.example.com/webSHAP.js URL中访问。
URL
https://arxiv.org/abs/2303.09545
