In the rapidly evolving landscape of artificial intelligence and machine learning, the black-box nature of complex algorithms poses a significant challenge to understanding and interpreting model decisions. As the deployment of these models becomes more pervasive, the demand for transparency and interpretability has surged. This article explores the intricate realm of explainable optimization techniques aimed at unraveling the mysteries of algorithms. We delve into various approaches that enhance model interpretability, empowering stakeholders to make informed decisions and build trust in the increasingly sophisticated AI systems.
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Global Journal of Technology and Optimization received 664 citations as per Google Scholar report
