Description
Explainable AI (XAI) is a crucial advancement in artificial intelligence that focuses on making AI systems transparent and interpretable. Many modern AI models, particularly deep learning algorithms, function as “black boxes,” making it difficult for users to understand how decisions are made. XAI addresses this challenge by providing insights into model behavior, ensuring fairness, accountability, and trustworthiness. This is especially important in fields like healthcare and finance, where AI-driven decisions can have significant real-world consequences. By offering explanations for predictions, XAI helps users develop confidence in AI models and enables better decision-making.
One of the widely used XAI techniques is LIME (Local Interpretable Model-agnostic Explanations), which explains individual predictions of complex models. LIME works by generating synthetic data around a given input and fitting a simpler, interpretable model to approximate the AI system’s decision-making process.
This allows users to identify which features influenced a particular prediction, making AI more transparent. LIME is particularly valuable in applications such as medical diagnosis, where understanding the reasoning behind a model’s decision is critical. With techniques like LIME, XAI is shaping the future of artificial intelligence, ensuring AI systems remain understandable, reliable, and aligned with human expectations.
