Just 'throwing' some metrics at your XAI explanations and consider the job done, is an approach not very productive. Before evaluating your explanations, make sure to:
- Always read the original publication to understand the context that the metric was introduced in - it may differ from your specific task and/ or data domain
- Spend time on understanding and investigate how the hyperparameters of the metrics influence the evaluation outcome; does changing the perturbation function fundamentally change scores?
- Establish evidence that your chosen metric is well-behaved in your specific setting e.g., include a random explanation (as a control variant) to verify the metric
- Reflect on the metric's underlying assumptions e.g., most perturbation-based metrics don't account for nonlinear interactions between features
- Ensure that your model is well-trained, a poor behaving model e.g., a non-robust model will have useless explanations