Several techniques and insights from the "Fairness without Demographics" literature can be leveraged to further enhance fairness in human-centered federated learning systems:
Instance Reweighting: Adopt instance reweighting techniques that focus on boosting instances that are underrepresented or have been subject to bias, without explicitly considering demographic attributes. This can help in achieving fairness without relying on sensitive information.
Knowledge Distillation: Explore knowledge distillation methods that transfer fairness-related knowledge from pre-trained models to federated learning models, without sharing sensitive attributes. This can help in improving fairness without compromising privacy.
Adversarial Learning: Implement adversarial learning approaches that introduce adversarial perturbations to the training process, aiming to reduce bias and enhance fairness without the need for demographic information.
Fairness Regularization: Introduce fairness regularization terms in the loss function that penalize unfair predictions or decisions, promoting fairness in the model's outputs across different groups or distributions.
Fairness-aware Aggregation: Develop aggregation strategies that prioritize models based on fairness metrics, such as equal opportunity or disparate impact, to ensure fairness in the final federated model without requiring demographic attributes.
By incorporating these techniques and insights from the "Fairness without Demographics" literature, human-centered federated learning systems can achieve enhanced fairness while upholding privacy and data protection principles.