When to choose ranked area integrals versus integrated gradient for explainable artificial intelligence – a comparison of algorithms

Abstract

Purpose

Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.

Design/methodology/approach

In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.

Findings

The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.

Research limitations/implications

A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.

Originality/value

Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.

Citation

Singh, V., Konovalova, I. and Kar, A.K. (2023), "When to choose ranked area integrals versus integrated gradient for explainable artificial intelligence – a comparison of algorithms", Benchmarking: An International Journal, Vol. 30 No. 9, pp. 3067-3089. https://doi.org/10.1108/BIJ-02-2022-0112