by Manjunatha Veerappa (Fraunhofer IOSB) and Salvo Rinzivillo (CNR-ISTI)
Artificial Intelligence (AI) has witnessed remarkable advancements in recent years, transforming various domains and enabling groundbreaking capabilities. However, the increasing complexity of AI models, such as convolutional neural networks (CNNs) and deep learning architectures, has raised concerns regarding their interpretability and explainability. As AI systems become integral to critical decision-making processes, it becomes essential to understand and trust the reasoning behind their outcomes. This need has given rise to the field of explainable AI (XAI), which focuses on developing methods and frameworks to enhance the interpretability and transparency of AI models, bridging the gap between accuracy and explainability.