WebSHAP: Towards Explaining Any Machine Learning Models Anywhere

• URL: http://arxiv.org/abs/2303.09545v1
• Date: Thu, 16 Mar 2023 17:56:02 GMT
• Title: WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
• Authors: Zijie J. Wang, Duen Horng Chau
• Abstract summary: 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.
• Score: 13.883867498610172
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/poloclub/webshap.

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