Explainable Artificial Intelligence Using Forward-Forward Networks: A Study Involving Quantitative Analysis - Fabris, Vitor L.; Oliveira, Juliane R. de; Silva, Camille H. B.; Cassenote, Vanessa; Silva, José V. N. A. da; Arrais, Rodrigo R.; Paris, Renata De (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
The field of eXplainable Artificial Intelligence (XAI) aims to understand the output of machine learning algorithms. We observed that the literature faults in proposing the systematic evaluation of XAI metrics and requires human perception to evaluate. This paper assesses XAI methods using the Forward-Forward (FF) algorithm from Geoffrey Hinton’s proposal. Through a quantitative and critical analysis of XAI algorithms mainly SHAP, LIME, and Grad-CAM this study assesses the effectiveness of LIME by comparing ground truth image and LIME mask output using traditional evaluation metrics. Our contributions to this paper are to improve our understanding of the FF output using XAI and to provide a systematic strategy for evaluating XAI metrics. We demonstrate that the proposed metrics effectively highlight the features considered by the FF network when correctly or incorrectly classifying images, allowing for quantitative distinction.