REPROT: Explaining the predictions of complex deep learning architectures for object detection through reducts of an image Bello García, Marilyn Mesejo Santiago, Pablo Cordón García, Óscar Deep learning Visual explanation Rough set theory Reduct Prototype image Research funded by MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under grant CONFIA (PID2021-122916NB-I00). Although deep learning models can solve complex prediction problems, they have been criticized for being ‘black boxes’. This implies that their decisions are difficult, if not impossible, to explain by simply inspecting their internal knowledge structures. Explainable Artificial Intelligence has attempted to open the black-box through model-specific and agnostic post-hoc methods that generate visualizations or derive associations between the problem features and the model predictions. This paper proposes a new method, termed REPROT, that explains the decisions of complex deep learning architectures based on local reducts of an image. A ‘reduct’ is a set of sufficiently descriptive features that can fully characterize the acquired knowledge. The created reducts are used to build a ‘prototype image’ that visually explains the inference obtained by a black-box model for an image. We focus on deep learning architectures whose complexity and internal particularities demand adapting existing model-specific explanation methods, making the explanation process more difficult. Experimental results show that the black-box model can detect an object using the prototype image generated from the reduct. Hence, the explanations will be given by “the minimum set of features sufficient for the neural model to detect an object”. The confidence scores obtained by architectures such as Inception, Yolo, and Mask R-CNN are higher for prototype images built from the reduct than those built from the most important superpixels according to the LIME method. Moreover, the target object is not detected on several occasions through the LIME output, thus supporting the superiority of the proposed explanation method. 2023-11-21T12:47:57Z 2023-11-21T12:47:57Z 2024-01 journal article Bello, G. Nápoles, L. Concepción et al. REPROT: Explaining the predictions of complex deep learning architectures for object detection through reducts of an image. Information Sciences 654 (2024) 119851. [https://doi.org/10.1016/j.ins.2023.119851] https://hdl.handle.net/10481/85813 10.1016/j.ins.2023.119851 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier