To extend the proposed fair-aware data augmentation method for a broader range of non-sensitive attributes, we can consider several strategies. First, we can identify additional non-sensitive attributes that may correlate with sensitive attributes, such as user interests, browsing history, location data, and demographic indicators like age or occupation. By analyzing these attributes, we can create a more comprehensive dataset that reflects diverse user profiles.
Next, we can implement a multi-faceted approach to data augmentation. This could involve generating synthetic data that simulates various combinations of non-sensitive attributes while maintaining the integrity of the original dataset. For instance, we could use generative models to create user profiles that include a mix of interests, locations, and other demographic factors, ensuring that the augmented data reflects a wide range of potential user scenarios.
Additionally, we can employ clustering techniques to group users based on their non-sensitive attributes and then generate counterfactual samples from these clusters. This would allow us to create a more balanced representation of different user profiles, thereby enhancing the model's ability to mitigate implicit ranking unfairness across various demographic groups.
Finally, integrating feedback loops from real-world applications can help refine the augmentation process. By continuously monitoring the performance of LLMs in real-world scenarios and adjusting the augmentation strategies based on observed biases, we can ensure that the method remains effective and relevant in addressing implicit ranking unfairness.