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

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method