@inproceedings{verma-etal-2020-defining,
title = "Defining Explanation in an {AI} Context",
author = "Verma, Tejaswani and
Lingenfelder, Christoph and
Klakow, Dietrich",
editor = "Alishahi, Afra and
Belinkov, Yonatan and
Chrupa{\l}a, Grzegorz and
Hupkes, Dieuwke and
Pinter, Yuval and
Sajjad, Hassan",
booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.blackboxnlp-1.29",
doi = "10.18653/v1/2020.blackboxnlp-1.29",
pages = "314--322",
abstract = "With the increase in the use of AI systems, a need for explanation systems arises. Building an explanation system requires a definition of explanation. However, the natural language term explanation is difficult to define formally as it includes multiple perspectives from different domains such as psychology, philosophy, and cognitive sciences. We study multiple perspectives and aspects of explainability of recommendations or predictions made by AI systems, and provide a generic definition of explanation. The proposed definition is ambitious and challenging to apply. With the intention to bridge the gap between theory and application, we also propose a possible architecture of an automated explanation system based on our definition of explanation.",
}
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<abstract>With the increase in the use of AI systems, a need for explanation systems arises. Building an explanation system requires a definition of explanation. However, the natural language term explanation is difficult to define formally as it includes multiple perspectives from different domains such as psychology, philosophy, and cognitive sciences. We study multiple perspectives and aspects of explainability of recommendations or predictions made by AI systems, and provide a generic definition of explanation. The proposed definition is ambitious and challenging to apply. With the intention to bridge the gap between theory and application, we also propose a possible architecture of an automated explanation system based on our definition of explanation.</abstract>
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%0 Conference Proceedings
%T Defining Explanation in an AI Context
%A Verma, Tejaswani
%A Lingenfelder, Christoph
%A Klakow, Dietrich
%Y Alishahi, Afra
%Y Belinkov, Yonatan
%Y Chrupała, Grzegorz
%Y Hupkes, Dieuwke
%Y Pinter, Yuval
%Y Sajjad, Hassan
%S Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F verma-etal-2020-defining
%X With the increase in the use of AI systems, a need for explanation systems arises. Building an explanation system requires a definition of explanation. However, the natural language term explanation is difficult to define formally as it includes multiple perspectives from different domains such as psychology, philosophy, and cognitive sciences. We study multiple perspectives and aspects of explainability of recommendations or predictions made by AI systems, and provide a generic definition of explanation. The proposed definition is ambitious and challenging to apply. With the intention to bridge the gap between theory and application, we also propose a possible architecture of an automated explanation system based on our definition of explanation.
%R 10.18653/v1/2020.blackboxnlp-1.29
%U https://aclanthology.org/2020.blackboxnlp-1.29
%U https://doi.org/10.18653/v1/2020.blackboxnlp-1.29
%P 314-322
Markdown (Informal)
[Defining Explanation in an AI Context](https://aclanthology.org/2020.blackboxnlp-1.29) (Verma et al., BlackboxNLP 2020)
ACL
- Tejaswani Verma, Christoph Lingenfelder, and Dietrich Klakow. 2020. Defining Explanation in an AI Context. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 314–322, Online. Association for Computational Linguistics.