Shortcomings of Interpretability Taxonomies for Deep Neural Networks

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Shortcomings of Interpretability Taxonomies for Deep Neural Networks. / Søgaard, Anders.

In: CEUR Workshop Proceedings, Vol. 3318, 2022.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Søgaard, A 2022, 'Shortcomings of Interpretability Taxonomies for Deep Neural Networks', CEUR Workshop Proceedings, vol. 3318.

APA

Søgaard, A. (2022). Shortcomings of Interpretability Taxonomies for Deep Neural Networks. CEUR Workshop Proceedings, 3318.

Vancouver

Søgaard A. Shortcomings of Interpretability Taxonomies for Deep Neural Networks. CEUR Workshop Proceedings. 2022;3318.

Author

Søgaard, Anders. / Shortcomings of Interpretability Taxonomies for Deep Neural Networks. In: CEUR Workshop Proceedings. 2022 ; Vol. 3318.

Bibtex

@inproceedings{b9b344572a8549ddb663dd6840c5bdf0,
title = "Shortcomings of Interpretability Taxonomies for Deep Neural Networks",
abstract = "Taxonomies are vehicles for thinking about what{\textquoteright}s possible, for identifying unconsidered options, as well as for establishing formal relations between entities. We identify several shortcomings in 10 existing taxonomies for interpretability methods for explainable artificial intelligence (XAI), focusing on methods for deep neural networks. The shortcomings include redundancies, incompleteness, and inconsistencies. We design a new taxonomy based on two orthogonal dimensions and show how it can be used to derive results about entire classes of interpretability methods for deep neural networks.",
keywords = "interpretability, taxonomy",
author = "Anders S{\o}gaard",
note = "Publisher Copyright: {\textcopyright} 2022 Copyright for this paper by its authors.; 2022 International Conference on Information and Knowledge Management Workshops, CIKM-WS 2022 ; Conference date: 17-10-2022 Through 21-10-2022",
year = "2022",
language = "English",
volume = "3318",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "ceur workshop proceedings",

}

RIS

TY - GEN

T1 - Shortcomings of Interpretability Taxonomies for Deep Neural Networks

AU - Søgaard, Anders

N1 - Publisher Copyright: © 2022 Copyright for this paper by its authors.

PY - 2022

Y1 - 2022

N2 - Taxonomies are vehicles for thinking about what’s possible, for identifying unconsidered options, as well as for establishing formal relations between entities. We identify several shortcomings in 10 existing taxonomies for interpretability methods for explainable artificial intelligence (XAI), focusing on methods for deep neural networks. The shortcomings include redundancies, incompleteness, and inconsistencies. We design a new taxonomy based on two orthogonal dimensions and show how it can be used to derive results about entire classes of interpretability methods for deep neural networks.

AB - Taxonomies are vehicles for thinking about what’s possible, for identifying unconsidered options, as well as for establishing formal relations between entities. We identify several shortcomings in 10 existing taxonomies for interpretability methods for explainable artificial intelligence (XAI), focusing on methods for deep neural networks. The shortcomings include redundancies, incompleteness, and inconsistencies. We design a new taxonomy based on two orthogonal dimensions and show how it can be used to derive results about entire classes of interpretability methods for deep neural networks.

KW - interpretability, taxonomy

M3 - Conference article

AN - SCOPUS:85146255048

VL - 3318

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 2022 International Conference on Information and Knowledge Management Workshops, CIKM-WS 2022

Y2 - 17 October 2022 through 21 October 2022

ER -

ID: 336294291