Explainable AI (XAI) is a field of research in artificial intelligence (AI) that focuses on making machine learning models. It is more transparent and understandable to humans. The goal of Explainable AI (XAI) is to enable users to understand the reasoning behind the decisions made by AI models. It also used to identify and correct any biases or errors in the models.
Traditional machine learning models, such as deep neural networks, can be difficult to interpret. They are highly complex and often involve many layers of abstraction. As a result, it can be challenging to understand why a particular decision was made. Especially if the model has not been properly validated.
XAI techniques aim to address this challenge by providing more transparency and interpretability in machine learning models. These techniques include methods for visualizing and explaining the decision-making process of a model, as well as techniques for identifying and mitigating biases in the data.
Some common techniques used in XAI include:
- Interpretable models: Using simpler, more transparent models such as decision trees or linear models, which are easier to understand and interpret.
- Feature importance: Identifying which features of the input data are most important for the model’s decision-making process.
- Counterfactual explanations: Generating examples of input data that would result in different outcomes, to explain the decision-making process of the model.
- Attention mechanisms: Highlight which parts of the input data the model is paying attention to during the decision-making process.
- Model-agnostic methods: It refer to techniques that apply to any type of machine-learning model. For example LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive exPlanations).
AI models are now used in crucial applications, making XAI more important than ever before. Such as healthcare, finance, and law enforcement. By providing more transparency and interpretability in these models.