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We will first review previous work on explainable AI methods and present two works on this topic. We will first present Opti-CAM, a CAM-based method that optimizes a masking objective per instance. The resulting saliency map highlights the important area of an image regarding the decision of a trained image classification network. Then, we will focus on image classification using parts or prototype-based architectures. Such model is explainable by design and brings transparency to a model.