Author Affiliation

Department of and Engineering Management and Systems Engineering, Old Dominion University

Publication Date

2022

Conference Title

Modeling, Simulation and Visualization Student Capstone Conference 2022

Conference Track

General Sciences & Engineering

Document Type

Paper

Abstract

The explainability of a model has been a topic of debate. Some research states explainability is unnecessary, and some ”white-box” models, such as regression models or decision trees, are inherently explainable. This paper conducts a multiple regression model analysis with highly correlated features to illustrate how the model’s explainability fails when dealing with complex data. In this case, trusting the model explanations can be problematic. The Shapley net effect technique, which helps determine the marginal contribution of the features, is employed to improve the model explainability and reveal more information about the prediction. The work concludes that explainability is necessary to avoid biased and erroneous conclusions in all circumstances, including simple models or even more apparent cases.

Keywords:

Machine learning model, Explainability, Explainable AI, Feature importance

Start Date

4-14-2022

End Date

4-14-2022

DOI

10.25776/2ta8-8058

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Apr 14th, 12:00 AM Apr 14th, 12:00 AM

Is Explainability Always Necessary? Discussion on Explainable AI

The explainability of a model has been a topic of debate. Some research states explainability is unnecessary, and some ”white-box” models, such as regression models or decision trees, are inherently explainable. This paper conducts a multiple regression model analysis with highly correlated features to illustrate how the model’s explainability fails when dealing with complex data. In this case, trusting the model explanations can be problematic. The Shapley net effect technique, which helps determine the marginal contribution of the features, is employed to improve the model explainability and reveal more information about the prediction. The work concludes that explainability is necessary to avoid biased and erroneous conclusions in all circumstances, including simple models or even more apparent cases.