Assessing the Fidelity of Explanations with Global Sensitivity Analysis

Date

2023-01-03

Contributor

Advisor

Department

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

Volume

Number/Issue

Starting Page

1085

Ending Page

Alternative Title

Abstract

Many explainability methods have been proposed as a means of understanding how a learned machine learning model makes decisions and as an important factor in responsible and ethical artificial intelligence. However, explainability methods often do not fully and accurately describe a model's decision process. We leverage the mathematical framework of global sensitivity analysis techniques to reveal deficiencies of explanation methods. We find that current explainaiblity methods fail to capture prediction uncertainty and make several simplifying assumptions that have significant ramifications on the accuracy of the resulting explanations. We show that the simplifying assumptions result in explanations that: (1) fail to model nonlinear interactions in the model and (2) misrepresent the importance of correlated features. Experiments suggest that failing to capture nonlinear feature interaction has a larger impact on the accuracy of the explanations. Thus, as most state-of-the-art ML models have non-linear interactions and operate on correlated data, explanations should only be used with caution.

Description

Keywords

Explainable Artificial Intelligence (XAI), artificial intelligence, explainability, fidelity, machine learning, sensitivity analysis

Citation

Extent

10

Format

Geographic Location

Time Period

Related To

Proceedings of the 56th Hawaii International Conference on System Sciences

Related To (URI)

Table of Contents

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.