Interpretability versus explainability: classification for understanding deep learning systems and models

• Autorzy: Namatēvs Ivars , Sudars Kaspars , Dobrājs Artis

Identyfikatory

DOI

10.24423/cames.518

• Języki publikacji: EN

Abstrakty

EN

The techniques of explainability and interpretability are not alternatives for many realworld problems, as recent studies often suggest. Interpretable machine learning is nota subset of explainable artificial intelligence or vice versa. While the former aims to build glass-box predictive models, the latter seeks to understand a black box using an explanatory model, a surrogate model, an attribution approach, relevance importance, or other statistics. There is concern that definitions, approaches, and methods do not match, leading to the inconsistent classification of deep learning systems and models for interpretation and explanation. In this paper, we attempt to systematically evaluate and classify the various basic methods of interpretability and explainability used in the field of deep learning.One goal of this paper is to provide specific definitions for interpretability and explainability in Deep Learning. Another goal is to spell out the various research methods for interpretability and explainability through the lens of the literature to create a systematic classifier for interpretability and explainability in deep learning. We present a classifier that summarizes the basic techniques and methods of explainability and interpretability models. The evaluation of the classifier provides insights into the challenges of developinga complete and unified deep learning framework for interpretability and explainability concepts, approaches, and techniques.

Słowa kluczowe

EN

explainable artificial intelligence; interpretable machine learning; deep learning; deep neural network; interpretability; explainability

PL

wytłumaczalna sztuczna inteligencja; interpretowalne uczenie maszynowe; głęboka nauka; głębokie sieci neuronowe; interpretowalność; wytłumaczalność

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2022
• Tom: Vol. 29, no. 4
• Strony: 297--356
• Opis fizyczny: Bibliogr. 237 poz., tab.

Twórcy

autor

Namatēvs Ivars

• Institute of Electronics and Computer Science

autor

Sudars Kaspars

• Institute of Electronics and Computer Science

autor

Dobrājs Artis

• Mondot Ltd.

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).