Report on Current Developments in Fairness and Bias Mitigation in Machine Learning
General Direction of the Field
The recent advancements in the field of fairness and bias mitigation in machine learning are primarily focused on developing innovative techniques that do not rely on privileged information, such as explicit sensitive attributes or extensive hyperparameter tuning. These developments aim to address the intrinsic trade-offs between fairness and model performance, particularly in scenarios where data privacy concerns limit access to sensitive attributes. The field is moving towards more dynamic and adaptive methods that can handle class imbalances, mitigate confirmation bias in semi-supervised learning, and ensure fairness without the need for sensitive attributes. Additionally, there is a growing emphasis on the generation and utilization of debiased pseudo-labels in semi-supervised learning, as well as the importance of fair synthetic data generation in data-free scenarios.
Key Innovations and Advances
Hyperparameter-Free Bias Mitigation: The introduction of hyperparameter-free frameworks that leverage the entire training history of a model to identify and mitigate bias is a significant advancement. These methods generate group-balanced training sets without requiring explicit group labels, thereby improving worst-group performance while maintaining overall accuracy.
Dynamic Fairness-Performance Trade-offs: New methods for computing the optimal Pareto front between fairness and performance are emerging, reducing the complexity of representation learning models. These approaches enable efficient computation of fairness-performance trade-offs, providing a benchmark for evaluating representation learning algorithms.
Debiased Training in Semi-Supervised Learning: The development of unified frameworks for debiased training in semi-supervised learning, such as TaMatch, is crucial. These frameworks dynamically adjust the influence of biased classes on parameter updates, enhancing training equity and minimizing class bias.
Dynamic Multiobjective Evolutionary Learning: The proposal of dynamically determining a representative set of fairness measures during model training is a notable innovation. This approach adapts the optimization objectives of multiobjective evolutionary learning frameworks, leading to more effective fairness mitigation.
Fairness without Sensitive Attributes: The introduction of confidence-based hierarchical classifier structures, such as Reckoner, addresses the challenge of learning fair models without access to sensitive attributes. These methods leverage high-confidence data subsets to avoid biased predictions, outperforming state-of-the-art baselines in accuracy and fairness metrics.
Fair Synthetic Data Generation: The development of generative models like Fair4Free, which generate high-fidelity fair synthetic samples using data-free distillation, is a promising direction. These models enhance fairness, utility, and synthetic quality, particularly in scenarios where access to training data is restricted.
Noteworthy Papers
Efficient Bias Mitigation Without Privileged Information: Introduces a hyperparameter-free framework that leverages the entire training history of a helper model to generate a group-balanced training set, outperforming existing methods.
Efficient Fairness-Performance Pareto Front Computation: Proposes a new method to compute the optimal Pareto front without the need for complex representation models, providing a benchmark for evaluating fairness-performance trade-offs.
Towards the Mitigation of Confirmation Bias in Semi-supervised Learning: Introduces TaMatch, a unified framework for debiased training in SSL, significantly outperforming state-of-the-art methods across challenging image classification tasks.
Fairness-aware Multiobjective Evolutionary Learning: Proposes dynamically determining a representative set of fairness measures during model training, achieving outstanding performance in mitigating unfairness.
Fairness without Sensitive Attributes via Knowledge Sharing: Introduces Reckoner, a confidence-based hierarchical classifier structure, consistently outperforming state-of-the-art baselines in accuracy and fairness metrics.
Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation: Presents a novel generative model that generates high-fidelity fair synthetic samples, outperforming state-of-the-art models in fairness, utility, and synthetic quality.
