The Challenge of Imputation in Explainable Artificial Intelligence Models

Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly infl...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Ahmad, Muhammad Aurangzeb, Eckert, Carly, Teredesai, Ankur
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Explainable artificial intelligence
Online Access:Get full text
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Summary:Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly influence explanation fidelity. The standard way to deal with such scenarios is imputation. This can, however, lead to situations where the imputed values may correspond to a setting which refer to counterfactuals. Acting on explanations from AI models with imputed values may lead to unsafe outcomes. In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
ISSN:2331-8422