This research addresses the growing ‘black-box’ nature of AI by evaluating Explainable Artificial Intelligence (XAI) as a crucial bridge between model complexity and human trust. The paper provides a comprehensive analysis of model-specific, model-agnostic, and hybrid XAI methodologies across critical sectors like healthcare, finance, and industrial management. It highlights how transparency in decision-making can maximize societal benefits while addressing ethical dilemmas and scalability challenges. By examining transformative applications such as fraud detection and cancer diagnosis, the study outlines a future where AI results are not only precise but also responsible and interpretable. This work advocates for interdisciplinary collaborations and standardized evaluation metrics to foster a trust-based environment for advanced AI deployment.
2025 Journal,
https://link.springer.com/journal/44163