# Towards eXplainable AI for Mobility Data Science

arXiv: https://arxiv.org/abs/2307.08461 · HTML: https://mlsanity.com/paper/2307.08461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection](https://mlsanity.com/paper/2303.09660/) (2023)
- [Corpus-level and Concept-based Explanations for Interpretable Document Classification](https://mlsanity.com/paper/2004.13003/) (2020)
- [Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification](https://mlsanity.com/paper/2204.11855/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Explainability in Mobility Data Science through a combination of methods](https://mlsanity.com/paper/2312.00380/) (2023)

## Abstract (arXiv)

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.