Attention Mechanisms in Deep Learning : Towards Explainable Artificial Intelligence

Attention mechanisms have revolutionized Machine Learning (ML), particularly in Natural Language Processing (NLP). These mechanisms enable models to selectively focus on crucial parts of the input data, improving performance across tasks like machine translation and sentiment analysis. However, complex ML architectures often remain opaque. This paper explores how attention mechanisms offer a unique path towards Explainable Artificial Intelligence (XAI). By visualizing and analyzing where a model "attends", we can gain insights into which features or data components were most impactful for its predictions. This understanding facilitates model debugging, bias detection, and the development of more transparent AI systems. We discuss different types of attention mechanisms and their potential for explainability in various ML domains.