Related papers: An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

URL: http://arxiv.org/abs/2109.14653v1
Date: Wed, 29 Sep 2021 18:21:19 GMT
Title: An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness
Authors: Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant
Abstract summary: We describe an empirical study to evaluate multiple model types on various metrics along these dimensions on several datasets. Our results show that no particular model type performs well on all dimensions, and demonstrate the kinds of trade-offs involved in selecting models evaluated along multiple dimensions.
Score: 16.677541058361218
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimensions, such as fairness, explainability, adversarial robustness, and distribution shift. We describe an empirical study to evaluate multiple model types on various metrics along these dimensions on several datasets. Our results show that no particular model type performs well on all dimensions, and demonstrate the kinds of trade-offs involved in selecting models evaluated along multiple dimensions.

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