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 https://github.com/poloclub/webshap.

翻译：随着机器学习日益融入日常网络体验，可解释的透明化网络机器学习成为迫切需求。然而现有可解释性技术通常需要专用后端服务器，这限制了其在网络社区向低延迟、高隐私的浏览器内机器学习迁移过程中的实用性。为满足客户端可解释性解决方案的迫切需求，我们提出WebSHAP——首个将最先进的模型无关可解释技术SHAP适配至网络环境的浏览器内工具。该开源工具采用WebGL等现代网络技术开发，充分利用客户端硬件能力，可便捷集成至现有网络机器学习应用。我们通过解释基于机器学习的贷款审批决策场景演示WebSHAP。基于本项目，我们探讨了透明化网络机器学习的未来研究机遇与挑战。WebSHAP开源地址：https://github.com/poloclub/webshap。