skip to main content
10.1145/3518997.3534987acmconferencesArticle/Chapter ViewAbstractPublication PagespadsConference Proceedingsconference-collections
extended-abstract

Explainable Artificial Intelligence: Requirements for Explainability

Published: 10 June 2022 Publication History

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

[1]
Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6(2018), 52138–52160.
[2]
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).
[3]
Shirley Gregor and Izak Benbasat. 1999. Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS quarterly (1999), 497–530.
[4]
Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
[5]
Avi Rosenfeld and Ariella Richardson. 2019. Explainability in human–agent systems. Autonomous Agents and Multi-Agent Systems 33, 6 (2019), 673–705.

Cited By

View all
  • (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
  • (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

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
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.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2022

Check for updates

Author Tags

  1. Explainable artificial intelligence
  2. explainability
  3. information
  4. information visualization

Qualifiers

  • Extended-abstract
  • Research
  • Refereed limited

Conference

SIGSIM-PADS '22
Sponsor:

Acceptance Rates

Overall Acceptance Rate 398 of 779 submissions, 51%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)43
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (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
  • (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

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media