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Published in:
The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Towards Model-Agnostic Ensemble Explanations

Authors : Szymon Bobek, Paweł Bałaga, Grzegorz J. Nalepa

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Explainable Artificial Intelligence (XAI) methods form a large portfolio of different frameworks and algorithms. Although the main goal of all of explanation methods is to provide an insight into the decision process of AI system, their underlying mechanisms may differ. This may result in very different explanations for the same tasks. In this work, we present an approach that aims at combining several XAI algorithms into one ensemble explanation mechanism via quantitative, automated evaluation framework. We focus on model-agnostic explainers to provide most robustness and we demonstrate our approach on image classification task.

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Footnotes
Literature
1.
Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)CrossRef
2.
Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods (2018)
3.
Collaris, D., van Wijk, J.J.: Explainexplore: visual exploration of machine learning explanations. In: 2020 IEEE Pacific Visualization Symposium (PacificVis), pp. 26–35 (2020)
4.
Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a “right to explanation”. arXiv preprint arXiv:​1606.​08813 (2016)
5.
6.
7.
Lundberg, S.M., et al.: Explainable AI for trees: from local explanations to global understanding. CoRR, abs/1905.04610 (2019)
8.
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 4768–4777. Curran Associates Inc. (2017)
9.
Mohseni, S., Zarei, N., Ragan, E.D.: A multidisciplinary survey and framework for design and evaluation of explainable AI systems (2020)
10.
Mujkanovic, F., Doskoč, V., Schirneck, M., Schäfer, P., Friedrich, T.: timeXplain - a framework for explaining the predictions of time series classifiers (2020)
11.
Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for graph convolutional neural networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10764–10773 (2019)
12.
Raji, I.D., et al.: Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. CoRR, abs/2001.00973 (2020)
13.
Ribeiro, M.T., Singh, S., Guestrin, C.: “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, KDD 2016, pp. 1135–1144. Association for Computing Machinery, New York (2016)
14.
Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: Thirty-Second AAAI Conference on Artificial Intelligence. AAAI Publications (2018)
15.
16.
Schank, R.C.: Explanation: a first pass. In: Kolodner, J.L., Riesbeck, C.K. (eds.) Experience, Memory, and Reasoning, pp. 139–165. Lawrence Erlbaum Associates, Hillsdale (1986)
17.
Sokol, K., Flach, P.: One explanation does not fit all. KI - Künstliche Intelligenz 34(2), 235–250 (2020)CrossRef
18.
Sokol, K., Flach, P.A.: Explainability fact sheets: a framework for systematic assessment of explainable approaches. CoRR, abs/1912.05100 (2019)
19.
Sokol, K., Santos-Rodríguez, R., Flach, P.A.: FAT forensics: a python toolbox for algorithmic fairness, accountability and transparency. CoRR, abs/1909.05167 (2019)
20.
Vemulapalli, R., Agarwala, A.: A compact embedding for facial expression similarity. CoRR, abs/1811.11283 (2018)
21.
Verma, M., Ganguly, D.: LIRME: locally interpretable ranking model explanation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 1281–1284. Association for Computing Machinery, New York (2019)
22.
Yeh, C.-K., Hsieh, C.-Y., Suggala, A.S., Inouye, D.I., Ravikumar, P.: On the (in)fidelity and sensitivity for explanations (2019)
23.
Zhang, Z., Yang, F., Wang, H., Hu, X.: Contextual local explanation for black box classifiers. CoRR, abs/1910.00768 (2019)
Metadata
Title
Towards Model-Agnostic Ensemble Explanations
Authors
Szymon Bobek
Paweł Bałaga
Grzegorz J. Nalepa
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_4

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