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Smart city article details
| Title | Explainable Artificial Intelligence On Smart Human Mobility: A Comparative Study Approach |
|---|---|
| ID_Doc | 25366 |
| Authors | Rosa L.; Silva F.; Analide C. |
| Year | 2023 |
| Published | Lecture Notes in Networks and Systems, 585 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-23210-7_9 |
| Abstract | Explainable artificial intelligence has been used in several scientific fields to understand how and why a machine learning model makes its predictions. Its characteristics have allowed for greater transparency and outcomes in AI-powered decision-making. This building trust and confidence can be useful in human mobility research. This work provides a comparative study in terms of the explainability of artificial intelligence on smart human mobility in the context of a regression problem. Decision Tree, LIME, SHAP, and Seldon Alibi are explainable approaches to describe human mobility using a dataset generated from New York Services. Based on our results, all of these approaches present relevant indicators for our problem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Explainable artificial intelligence; Machine learning; Smart cities; Smart human mobility |
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