一句话：在市政污水处理真实案例中评估三种先进 XAI 工具，从预测精度与解释质量两方面考察其可用性。
本文关注可解释人工智能（XAI）工具在公共部门真实场景中的有效性，针对市政污水处理组织的合流制下水道溢流（CSO）预测问题展开案例研究。作者将三种先进 XAI 工具应用于深度学习预测模型，并采用数据驱动调查，结合利益相关者访谈的定性信息与定量指标，评估工具的预测准确性，以及所生成解释的简洁性、可靠性和洞察力。结果表明，这些 XAI 工具在多大程度上满足实际需求存在差异；同时，领域专家能够从复杂解释中获得与既有预期不同的新洞见。该工作为在公共部门部署和评价 XAI 工具提供了经验证据。
Artificial intelligence and deep learning are becoming increasingly prevalent in contemporary software solutions. Explainable artificial intelligence (XAI) tools attempt to address the black box nature of the deep learning models and make them more understandable to humans. In this work, we apply three state-of-the-art XAI tools in a real-world case study. Our study focuses on predicting combined sewer overflow events for a municipal wastewater treatment organization. Through a data driven inquiry, we collect both qualitative information via stakeholder interviews and quantitative measures. These help us assess the predictive accuracy of the XAI tools, as well as the simplicity, soundness, and insightfulness of the produced explanations. Our results not only show the varying degrees that the XAI tools meet the requirements, but also highlight that domain experts can draw new insights from complex explanations that may differ from their previous expectations.
DOI 原文 ·
@inproceedings{paperbot6358,
title = {XAI tools in the public sector: a case study on predicting combined sewer overflows},
author = {Nicholas Maltbie and Nan Niu and Matthew Van Doren and Reese Johnson},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468547}
}