Explainable ai in ml: the path to transparency and accountability
This paper introduces Explainable Artificial Intelligence (XAI) as a requisite solution for increasing the interpretability and trustworthiness of ML systems. This paper explores the importance of XAI through case studies in two key sectors: manufacturing and healthcare. The first case relates to a predictive maintenance application that uses XAI to anticipate the likelihood of machinery failure using a gradient-boosting decision tree, which provides detailed recommendations for optimizing productivity. The second case study is based on the healthcare sector.