Towards eXplainable AI for Mobility Data Science

Autor:	Jalali, Anahid, Graser, Anita, Heistracher, Clemens
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence F.2.2
Druh dokumentu:	Working Paper
Popis:	This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI studies, argue the need for comprehensible explanations with human-centered approaches, and outline a research path toward XAI for Mobility Data Science. Comment: 4 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.08461 Zobrazit plný text záznamu View this record from Arxiv