%0 Conference Paper %1 tritscher2020evaluation %A Tritscher, Julian %A Ring, Markus %A Schlr, Daniel %A Hettinger, Lena %A Hotho, Andreas %B Foundations of Intelligent Systems %C Cham %D 2020 %E Helic, Denis %E Leitner, Gerhard %E Stettinger, Martin %E Felfernig, Alexander %E Raś, Zbigniew W. %I Springer International Publishing %K 2020 data datasets evaluation myown tabular xai %P 422--430 %T Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data %X Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data. %@ 978-3-030-59491-6

@inproceedings{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, added-at = {2021-01-24T18:28:53.000+0100}, address = {Cham}, author = {Tritscher, Julian and Ring, Markus and Schlr, Daniel and Hettinger, Lena and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/28b5575da838904117581004f7301f6cb/hotho}, booktitle = {Foundations of Intelligent Systems}, description = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data | SpringerLink}, editor = {Helic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Ra{\'{s}}, Zbigniew W.}, interhash = {299b6ce3d3a46445fd2bfb1a6ee5baea}, intrahash = {8b5575da838904117581004f7301f6cb}, isbn = {978-3-030-59491-6}, keywords = {2020 data datasets evaluation myown tabular xai}, pages = {422--430}, publisher = {Springer International Publishing}, timestamp = {2021-01-24T22:43:11.000+0100}, title = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data}, year = 2020 }