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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

February 1, 2023

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Authors

Arjun Akula

University of California, Los Angeles


Shuai Wang

University of Illinois at Chicago


Song-Chun Zhu

University of California, Los Angeles


DOI:

10.1609/aaai.v34i03.5643


Abstract:

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

Topics: AAAI

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HOW TO CITE:

Arjun Akula||Shuai Wang||Song-Chun Zhu CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines Proceedings of the AAAI Conference on Artificial Intelligence (2020) 2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines AAAI 2020, 2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu (2020). CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines. Proceedings of the AAAI Conference on Artificial Intelligence, 2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu. CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu. 2020. CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines. "Proceedings of the AAAI Conference on Artificial Intelligence". 2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu. (2020) "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines", Proceedings of the AAAI Conference on Artificial Intelligence, p.2594-2601

Arjun Akula||Shuai Wang||Song-Chun Zhu, "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines", AAAI, p.2594-2601, 2020.

Arjun Akula||Shuai Wang||Song-Chun Zhu. "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu. "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 2594-2601.

Arjun Akula||Shuai Wang||Song-Chun Zhu. CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines. AAAI[Internet]. 2020[cited 2023]; 2594-2601.


ISSN: 2374-3468


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