Explainable AI, also known as XAI, refers to artificial intelligence systems whose actions and decisions can be easily understood by human observers. This is unlike typical AI systems that operate as a "black box" where the decision-making process is hidden from users. The main objective of explainable AI is to create a system that is transparent, interpretable, and trustworthy. It should be able to provide clear explanations for its decisions and actions in a format that humans can comprehend, regardless of their knowledge of AI.
Explainable AI in practice
Explainable AI operates by incorporating interpretability into the model. This is achieved by designing it in such a way that its inner workings can be visualized, examined and interpreted by humans.
In simpler models, like linear regression and decision trees, the decision-making process can be easily understandable. However, for complex machine learning models like neural networks, it's often difficult or even impossible to understand how the model makes a decision.
To make such models more explainable, methods like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) are used. LIME works by creating an interpretable model around the prediction then uses this model to explain how the original complex model works. On the other hand, SHAP assigns each feature an importance value for a specific prediction.
The goal of these methods is to provide transparency, increase trust, and make it possible to validate and debug AI systems. Explainable AI is crucial in sectors like healthcare, finance, or any other field where understanding why a certain decision was made by an AI system is important.
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