What does "Sample-based" mean?
Table of Contents
Sample-based explanations are methods used to clarify how complex models, like those in artificial intelligence, make decisions. Instead of showing the inner workings of a model directly, these methods take samples from data to generate understandable reasons for a model's outputs.
Features of Sample-based Explanations
These explanations aim to fulfill certain important traits. They should provide clear and consistent reasons, be easy to compute, and allow for comparisons between different models. Some sample-based explanation methods can produce reasons quickly, which is helpful when a decision needs to be explained instantly.
The Approach
To create sample-based explanations, the focus is on using data samples to derive reasons that relate to the model's decision. This process helps identify the best possible explanation while ensuring it matches the model's behavior. It works well, even when the model is complicated or when it's hard to interpret its logic directly.
Applications
Sample-based explanations are useful in various fields, especially in scientific research. For example, they can help scientists understand complex models based on experimental data, leading to better insights without losing important information. Overall, this approach balances clarity and complexity, making it easier to grasp how decisions are made.