Making AI models more transparent and understandable to humans involves using various techniques such as LIME and SHAP, which explain individual predictions and distribute feature importance fairly, respectively. Interpretable models like decision trees and rule-based systems, along with visualizations such as feature importance charts and saliency maps, also contribute to model transparency. Additionally, user interfaces like interactive dashboards and explanation interfaces enhance the comprehensibility of AI systems.

Benefits:

The benefits of Explainable AI (XAI) include building trust by enabling users to understand and verify AI decisions, ensuring accountability by making it easier to identify and correct errors or biases, meeting regulatory requirements for transparency, and improving decision-making by providing stakeholders with a clear understanding of the AI’s reasoning.