AAAI 2025technical0 citations
Counterfactual Explanations of Time Varying Rankings (Student Abstract)
Ryusei Ohtani, Yuko Sakurai, Satoshi Oyama
Abstract
Counterfactual explanations in Explainable AI (XAI) identify which features to change to alter an outcome, but existing methods adjust only the features of a single agent. We present a new approach to re-evaluating rankings that is based on predictions of future features of the other agents in a ranking system. It uses an algorithm that provides a more realistic counterfactual explanation of changing the ranking of a particular agent. Computer experiments demonstrated that the proposed algorithm can capture the time variation of the entire ranking system in the inference results.
BibTeX
@article{Ohtani_Sakurai_Oyama_2025, title={Counterfactual Explanations of Time Varying Rankings (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35285}, DOI={10.1609/aaai.v39i28.35285}, abstractNote={Counterfactual explanations in Explainable AI (XAI) identify which features to change to alter an outcome, but existing methods adjust only the features of a single agent. We present a new approach to re-evaluating rankings that is based on predictions of future features of the other agents in a ranking system. It uses an algorithm that provides a more realistic counterfactual explanation of changing the ranking of a particular agent. Computer experiments demonstrated that the proposed algorithm can capture the time variation of the entire ranking system in the inference results.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ohtani, Ryusei and Sakurai, Yuko and Oyama, Satoshi}, year={2025}, month={Apr.}, pages={29453-29455} }