可解释人工智能（XAI）

一、可解释人工智能（XAI）

• Molnar (2020). Interpretable machine learning: a guide for making black box models explainable. https://christophm.github.io/interpretable-ml-book/

• Royal Society. (2019).  "Explainable AI: the basics‐Policy briefing." https://royalsociety.org/-/media/policy/projects/explainable-ai/AI-and-interpretability-policy-briefing.pdf

• Gunning, D., & Aha, D. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI magazine, 40(2), 44-58. https://doi.org/10.1609/aimag.v40i2.2850

• Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI magazine, 38(3), 50-57. https://doi.org/10.1609/aimag.v38i3.2741

• Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. https://doi.org/10.1073/pnas.1900654116

• Das, A., & Rad, P. (2020). Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371. https://doi.org/10.48550/arXiv.2006.11371

• Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012

二、XAI的方法介绍

• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. https://doi.org/10.5555/3295222.3295230

• Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 56-67. https://doi.org/10.1038/s42256-019-0138-9

• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778

• Sundararajan, M., Taly, A., & Yan, Q. (2017, July). Axiomatic attribution for deep networks. In International conference on machine learning (pp. 3319-3328). PMLR. https://doi.org/10.5555/3305890.3306024

• Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618-626). https://doi.org/10.1109/ICCV.2017.74

• Covert, I., Lundberg, S. M., & Lee, S. I. (2020). Understanding global feature contributions with additive importance measures. Advances in Neural Information Processing Systems, 33, 17212-17223. https://doi.org/10.5555/3495724.3497168

三、XAI与地理学 & 四、机遇与挑战

• Fotheringham, A. S. (1997). Trends in quantitative methods I: stressing the local. Progress in human geography, 21(1), 88-96. https://doi.org/10.1191/030913297676693207

• Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.

• Fotheringham, A.S., Oshan, T.M., & Li, Z. (2023). Multiscale Geographically Weighted Regression: Theory and Practice (1st ed.). CRC Press.

• Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. https://doi.org/10.1016/j.compenvurbsys.2022.101845

• Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405. https://doi.org/10.1016/j.aap.2019.105405

• Hsu, C. Y., & Li, W. (2023). Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection. International Journal of Geographical Information Science, 37(5), 963-987. https://doi.org/10.1080/13658816.2023.2191256

• Xing, J., & Sieber, R. (2023). The challenges of integrating explainable artificial intelligence into GeoAI. Transactions in GIS. https://doi.org/10.1111/tgis.13045