Rule-based explainable AI: This type of AI system operates based on a predefined set of rules and logical reasoning. The decision-making process is transparent and explainable since the rules and conditions can be easily understood and interpreted. Examples include expert systems and decision trees.
Model-based explainable AI: In this approach, the AI system utilizes a transparent model to make predictions or decisions. The model can be a simple linear regression or a more complex ensemble of models. The transparency and explainability come from the ability to interpret the model's parameters and understand how they contribute to the system's outputs.
Example-based explainable AI: This type of AI system learns from a collection of labeled examples and generalizes from them to make predictions or decisions. The explainability lies in the ability to trace back the system's outputs to the specific examples that influenced the decision. Techniques such as prototype-based reasoning and case-based reasoning fall into this category.
Interpretable neural networks: Neural networks, particularly deep learning models, are known for their black-box nature. However, efforts have been made to develop techniques that enhance their explainability. Interpretable neural networks aim to provide insights into the decision-making process by incorporating additional structures or methods, such as attention mechanisms, saliency maps, or layer-wise relevance propagation (LRP).
Hybrid models: These are AI systems that combine multiple approaches to achieve explainability. For example, a hybrid model could incorporate rule-based components with a neural network to leverage the interpretability of rules and the learning capabilities of neural networks. Hybrid models can provide a balance between accuracy and transparency, making them useful in applications where explainability is crucial.
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