Exploring common pitfalls in prediction explanations and how to avoid them
Although prediction explanations have proven to be valuable tools in understanding AI models, several common pitfalls can hinder their effectiveness. In this section, we will discuss these pitfalls and provide strategies to avoid them, ensuring that prediction explanations remain a valuable resource for understanding and improving AI models. Some of the common pitfalls, along with their solutions, are as follows:
- Over-reliance on explanations: While prediction explanations can provide valuable insights into a model’s decision-making process, over-relying on these explanations can lead to incorrect conclusions. It’s important to remember that prediction explanations are just one piece of the puzzle and should be used in conjunction with other evaluation methods to gain a comprehensive understanding of a model’s performance. The solution here is to use a combination of evaluation methods...
