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Saliency Cards: A Framework to Characterize and Compare Saliency Methods

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Hendrik Strobelt,

Arvind SatyanarayanAuthors Info & Claims

FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Pages 285 - 296

https://doi.org/10.1145/3593013.3593997

Published: 12 June 2023 Publication History

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Abstract

Saliency methods are a common class of machine learning interpretability techniques that calculate how important each input feature is to a model’s output. We find that, with the rapid pace of development, users struggle to stay informed of the strengths and limitations of new methods and, thus, choose methods for unprincipled reasons (e.g., popularity). Moreover, despite a corresponding rise in evaluation metrics, existing approaches assume universal desiderata for saliency methods (e.g., faithfulness) that do not account for diverse user needs. In response, we introduce saliency cards: structured documentation of how saliency methods operate and their performance across a battery of evaluative metrics. Through a review of 25 saliency method papers and 33 method evaluations, we identify 10 attributes that users should account for when choosing a method. We group these attributes into three categories that span the process of computing and interpreting saliency: methodology, or how the saliency is calculated; sensitivity, or the relationship between the saliency and the underlying model and data; and, perceptibility, or how an end user ultimately interprets the result. By collating this information, saliency cards allow users to more holistically assess and compare the implications of different methods. Through nine semi-structured interviews with users from various backgrounds, including researchers, radiologists, and computational biologists, we find that saliency cards provide a detailed vocabulary for discussing individual methods and allow for a more systematic selection of task-appropriate methods. Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.

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Cited By

Turri VMorrison KRobinson KAbidi CPerer AForlizzi JDzombak R(2024)Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding ApproachesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658985(1494-1514)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658985
Fresz BLörcher LHuber M(2024)Classification Metrics for Image Explanations: Towards Building Reliable XAI-EvaluationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658537(1-19)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658537
Jahromi MMuddamsetty SJarlner AHøgenhaug AGammeltoft-Hansen TMoeslund T(2024)SIDU-TXT: An XAI algorithm for NLP with a holistic assessment approachNatural Language Processing Journal10.1016/j.nlp.2024.1000787(100078)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100078

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Saliency Cards: A Framework to Characterize and Compare Saliency Methods

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FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

June 2023

1929 pages

ISBN:9798400701924

DOI:10.1145/3593013

Copyright © 2023 Owner/Author.

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Published: 12 June 2023

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United States Air Force Research Laboratory and United States Air Force Artificial Intelligence Accelerator
MIT-IBM Watson AI Lab.

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FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency

June 12 - 15, 2023

IL, Chicago, USA

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Cited By

Turri VMorrison KRobinson KAbidi CPerer AForlizzi JDzombak R(2024)Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding ApproachesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658985(1494-1514)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658985
Fresz BLörcher LHuber M(2024)Classification Metrics for Image Explanations: Towards Building Reliable XAI-EvaluationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658537(1-19)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658537
Jahromi MMuddamsetty SJarlner AHøgenhaug AGammeltoft-Hansen TMoeslund T(2024)SIDU-TXT: An XAI algorithm for NLP with a holistic assessment approachNatural Language Processing Journal10.1016/j.nlp.2024.1000787(100078)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100078

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