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(ML)日益融入我们的日常网络经验中,人们呼吁采用透明和解释的网络ML。然而,现有的解释技术往往需要专门的后端服务器,这限制了它们的作用,因为网络社区为了较低的潜伏性和更大的隐私而转向在浏览者ML,从而限制其实用性。为了解决对客户端的可解释性解决方案的迫切需要,我们提出了WebSHAP,这是第一个将最先进的模型可解释性技术(SHAP)调整到网络环境的浏览者内部工具。我们开发的开放源工具使用现代网络技术,如WebGL, 利用客户端硬件能力,方便融入现有的网络ML应用程序。我们用WebSHAP向贷款申请者解释基于ML的贷款批准决定。我们思考我们的工作,我们讨论了在透明网络ML上进行未来研究的机会和挑战。WebSHAP可在https://github.com/poloclub/webshap上查阅。</s>