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Introduction to Explainable AI

Authors:

Q. Vera Liao,

Moninder Singh,

Yunfeng Zhang,

Rachel BellamyAuthors Info & Claims

CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 127, Pages 1 - 3

https://doi.org/10.1145/3411763.3445016

Published: 08 May 2021 Publication History

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Abstract

As Artificial Intelligence technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations to allow users and stakeholders to understand the AI has become a ubiquitous concern. Recently, a number of open-source toolkits are making the growing collection of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic, aiming to provide an overview on the technical and design methods for XAI, as well as hands-on experience with an XAI toolkit.

References

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Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arXiv preprint arXiv:1909.03012(2019).

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Cited By

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Jinad RIslam AShashidhar N(2024)Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial IntelligenceElectronics10.3390/electronics1313243813:13(2438)Online publication date: 21-Jun-2024
https://doi.org/10.3390/electronics13132438
Lehnert AGawantka FDuring JJust FReichenbach M(2024)XplAInable: Explainable AI Smoke Detection at the EdgeBig Data and Cognitive Computing10.3390/bdcc80500508:5(50)Online publication date: 17-May-2024
https://doi.org/10.3390/bdcc8050050
Gawantka FJust FSavelyeva MWappler MLässig J(2024)A Novel Metric for Evaluating the Stability of XAI ExplanationsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0901139(133-142)Online publication date: Feb-2024
https://doi.org/10.25046/aj090113
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Published In

CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

2965 pages

ISBN:9781450380959

DOI:10.1145/3411763

Editors:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Katherine Isbister
University of California Santa Cruz, USA
,
Takeo Igarashi
The University of Tokyo, Japan

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

Publication History

Published: 08 May 2021

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CHI '21

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SIGCHI

CHI '21: CHI Conference on Human Factors in Computing Systems

May 8 - 13, 2021

Yokohama, Japan

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Jinad RIslam AShashidhar N(2024)Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial IntelligenceElectronics10.3390/electronics1313243813:13(2438)Online publication date: 21-Jun-2024
https://doi.org/10.3390/electronics13132438
Lehnert AGawantka FDuring JJust FReichenbach M(2024)XplAInable: Explainable AI Smoke Detection at the EdgeBig Data and Cognitive Computing10.3390/bdcc80500508:5(50)Online publication date: 17-May-2024
https://doi.org/10.3390/bdcc8050050
Gawantka FJust FSavelyeva MWappler MLässig J(2024)A Novel Metric for Evaluating the Stability of XAI ExplanationsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0901139(133-142)Online publication date: Feb-2024
https://doi.org/10.25046/aj090113
Nimmo RConstantinides MZhou KQuercia DStumpf S(2024)User Characteristics in Explainable AI: The Rabbit Hole of Personalization?Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642352(1-13)Online publication date: 11-May-2024
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Bharati SMondal MPodder P(2024)A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?IEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32664185:4(1429-1442)Online publication date: Apr-2024
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Chen CSundar S(2023)Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User TrustProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580805(1-11)Online publication date: 19-Apr-2023
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