Explainable AI: Interpreting and Understanding Machine Learning Models

Explainable Artificial Intelligence (AI) has emerged as a field of study that aims to provide transparency and interpretability to machine learning models. As AI algorithms become increasingly complex and pervasive in various domains, the ability to understand and interpret their decisions becomes crucial for ensuring fairness, accountability, and trustworthiness. This abstract provides an overview of the importance of explainable AI and highlights some of the key techniques and approaches used in interpreting and understanding machine learning models. The abstract begins by emphasizing the growing significance of explainability in AI systems. As machine learning models are deployed in critical applications such as healthcare, finance, and autonomous vehicles, it becomes essential to comprehend the reasoning behind their predictions. Explainable AI methods provide insights into how these models arrive at their decisions, enabling stakeholders to identify biases, diagnose errors, and gain actionable insights from the model's behavior.