Semi-factual Explanations in AI
Abstract
Most of the recent works on post-hoc example-based eXplainable AI (XAI) methods revolves around employing counterfactual explanations to provide justification of the predictions made by AI systems. Counterfactuals show what changes to the input-features change the output decision. However, a lesser-known, special-case of the counterfacual is the semi-factual, which provide explanations about what changes to the input-features do not change the output decision. Semi-factuals are potentially as useful as counterfactuals but have received little attention in the XAI literature. My doctoral research aims to establish a comprehensive framework for the use of semi-factuals in XAI by developing novel methods for their computation, supported by user tests.
BibTeX
@article{Aryal_2024, title={Semi-factual Explanations in AI}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30390}, DOI={10.1609/aaai.v38i21.30390}, abstractNote={Most of the recent works on post-hoc example-based eXplainable AI (XAI) methods revolves around employing counterfactual explanations to provide justification of the predictions made by AI systems. Counterfactuals show what changes to the input-features change the output decision. However, a lesser-known, special-case of the counterfacual is the semi-factual, which provide explanations about what changes to the input-features do not change the output decision. Semi-factuals are potentially as useful as counterfactuals but have received little attention in the XAI literature. My doctoral research aims to establish a comprehensive framework for the use of semi-factuals in XAI by developing novel methods for their computation, supported by user tests.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Aryal, Saugat}, year={2024}, month={Mar.}, pages={23379-23380} }