Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Difference and Uniqueness (SIDU) method that can effectively localize entire object regions responsible for prediction is presented in full detail. The SIDU algorithm robustness and effectiveness is analyzed through various computational and human subject experiments. In particular, the SIDU algorithm is assessed using three different types of evaluations (Application, Human and Functionally-Grounded) to demonstrate its superior performance. The robustness of SIDU is further studied in the presence of adversarial attack on "black-box" models to better understand its performance. Our code is available at: https://github.com/satyamahesh84/SIDU_XAI_CODE.

翻译：近年来,可解释的人工智能(XAI)已经成为一个非常合适的框架,对“黑盒”模型作出人所理解的解释。在本文中,全面详细介绍了名为“相似差异和独特性(SIDU)”的新型XAI直观解释算法,该算法可以有效地将负责预测的整个目标区域本地化。SIDU算法的稳健性和有效性通过各种计算和人类实验进行分析。特别是,SIDU算法的评估使用三种不同的评价(应用、人和功能四面八方)来显示其优异的性能。SITU的稳健性在对“黑盒”模型的对抗性攻击面前得到进一步研究,以更好地了解其性能。我们的代码可在以下网址查阅:https://github.com/satyamahesh84/SIDU_XAI_CODE。