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Explainable Artificial Intelligence: Requirements for Explainability

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Gayane GrigoryanAuthors Info & Claims

SIGSIM-PADS '22: Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Pages 27 - 28

https://doi.org/10.1145/3518997.3534987

Published: 10 June 2022 Publication History

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Abstract

To date, many reasons have been suggested for making explainable artificial intelligence (XAI) models. However, it is unclear when the XAI suggested content is considered an explanation. This paper conducts a survey to determine the requirements for the information to be considered an explanation. Four minimum requirements have been prioritized based on the impact of the change they present to distinguishing between information and explainability.

References

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Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6(2018), 52138–52160.

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Derek Doran, Sarah Schulz, and Tarek R Besold. 2017. What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794(2017).

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Shirley Gregor and Izak Benbasat. 1999. Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS quarterly (1999), 497–530.

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Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).

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Avi Rosenfeld and Ariella Richardson. 2019. Explainability in human–agent systems. Autonomous Agents and Multi-Agent Systems 33, 6 (2019), 673–705.

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Grigoryan G(2024)Explainable Artificial Intelligence for Simulation ModelsProceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3615979.3662148(59-60)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3615979.3662148
Grigoryan GCollins A(2023)Feature Importance for Uncertainty Quantification In Agent-Based Modeling2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10408477(233-242)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10408477

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Published In

SIGSIM-PADS '22: Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

June 2022

144 pages

ISBN:9781450392617

DOI:10.1145/3518997

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 10 June 2022

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SIGSIM

SIGSIM-PADS '22: SIGSIM Conference on Principles of Advanced Discrete Simulation

June 8 - 10, 2022

GA, Atlanta, USA

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Grigoryan G(2024)Explainable Artificial Intelligence for Simulation ModelsProceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3615979.3662148(59-60)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3615979.3662148
Grigoryan GCollins A(2023)Feature Importance for Uncertainty Quantification In Agent-Based Modeling2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10408477(233-242)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10408477

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Explainable artificial intelligence, lawyer's perspective

A manifesto on explainability for artificial intelligence in medicine

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