Description: Model interpretation is a fundamental process in the field of explainable artificial intelligence (XAI), which seeks to unravel how a machine learning model arrives at its predictions or decisions. This process involves breaking down the model’s decisions into understandable components, allowing users to grasp the reasons behind each outcome. Model interpretation not only focuses on the accuracy of predictions but also on the transparency and trust that users can have in the system. As AI models become more complex, the need to interpret them becomes critical, especially in sensitive applications such as healthcare, law enforcement, and finance. Interpretation can take various forms, from visualizations that show the importance of different features to techniques that explain the model’s behavior in specific situations. In summary, model interpretation is essential to ensure that AI systems are accessible and accountable, promoting a more informed interaction between humans and machines.
