I'd recommend looking at your model's performance sliced by different subgroups of the dataset to see on which groups the model is performance best/worst at. And then trying to find more training data in those areas of concern to get a better trained model.
In general, fairness evaluation for regression models is not as simple as binary classification, as many of the techniques and analysis depend on having a positive and negative class to investigate. Another approach is to turn your regression problem into a classification problem and using the binary classification fairness tools to understand more about your model in that context.
I'd recommend looking at your model's performance sliced by different subgroups of the dataset to see on which groups the model is performance best/worst at. And then trying to find more training data in those areas of concern to get a better trained model.
In general, fairness evaluation for regression models is not as simple as binary classification, as many of the techniques and analysis depend on having a positive and negative class to investigate. Another approach is to turn your regression problem into a classification problem and using the binary classification fairness tools to understand more about your model in that context.