WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
| Autor: | Zijie J. Wang, Duen Horng Chau |
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| Rok vydání: | 2023 |
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| DOI: | 10.48550/arxiv.2303.09545 |
| Popis: | 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. Comment: 5 pages, 4 figures. Accepted at the ACM Web Conference 2023 (WWW 2023). For a live demo, visit https://poloclub.github.io/webshap/. Code is open-source at https://github.com/poloclub/webshap |
| Databáze: | OpenAIRE |
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