From the course: Learning XAI: Explainable Artificial Intelligence

XAI techniques

From the course: Learning XAI: Explainable Artificial Intelligence

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XAI techniques

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- [Instructor] Though the field is early and rapidly changing, three of the more advanced XAI techniques are LIME, RETAIN, and LRP. Currently, the most common XAI technique is LIME. It stands for Local Interpretable Model-Agnostic Explanations. LIME is actually a post hoc model, which means that it is a technique that looks for an explanation after the decision has been made. One benefit of LIME is that, as its name states, it is model agnostic, meaning it doesn't matter what type of model it is applied to. The LIME approach involves perturbing or slightly changing the inputs of the model to observe how the outputs change. This allows us to understand which inputs affect the outputs the most, giving us insights into how the model made its decisions. RETAIN, which stands for Reversed Time Attention model, was developed at the Georgia Institute of Technology to study models that predicted heart failure. In this method, the model is designed such that, using the data from patient clinical visits, it can predict the occurrence of heart failure at a rate comparable to other models, but also identify which piece of clinical data contributed to the prediction. LRP, or layer-wise relevance propagation, works backwards through a neural network and figures out which input values were the most relevant in coming up with the output. Though these are three common XAI techniques being worked on today, there will no doubt be many others to come as the field develops. In the next two videos, we'll talk about why you might need XAI from the business and legal perspectives.

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