Fairness Under Demographic Scarce Regime

Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available because a record was not maintained throughout data collection or due to privacy reasons. This setting is known as demographic scarce regime. Prior research have shown that training an attribute classifier to replace the missing sensitive attributes (proxy) can still improve fairness. However, the use of proxy-sensitive attributes worsens fairness-accuracy trade-offs compared to true sensitive attributes. To address this limitation, we propose a framework to build attribute classifiers that achieve better fairness-accuracy trade-offs. Our method introduces uncertainty awareness in the attribute classifier and enforces fairness on samples with demographic information inferred with the lowest uncertainty. We show empirically that enforcing fairness constraints on samples with uncertain sensitive attributes is detrimental to fairness and accuracy. Our experiments on two datasets showed that the proposed framework yields models with significantly better fairness-accuracy trade-offs compared to classic attribute classifiers. Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.Comment: 14 pages, 7 page

Adversarial Stacked Auto-Encoders for Fair Representation Learning

Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data. One solution is to learn latent representations that fulfill specific fairness metrics. Different types of learning methods are employed to map data into the fair representational space. The main purpose is to learn a latent representation of data that scores well on a fairness metric while maintaining the usability for the downstream task. In this paper, we propose a new fair representation learning approach that leverages different levels of representation of data to tighten the fairness bounds of the learned representation. Our results show that stacking different auto-encoders and enforcing fairness at different latent spaces result in an improvement of fairness compared to other existing approaches.Comment: ICML2021 ML4data Workshop Pape

On the Fairness of Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data, and then sample synthetic realistic data. Many applications have emerged, using GANs to solve classical problems in machine learning, such as data augmentation, class unbalance problems, and fair representation learning. In this paper, we analyze and highlight fairness concerns of GANs model. In this regard, we show empirically that GANs models may inherently prefer certain groups during the training process and therefore they're not able to homogeneously generate data from different groups during the testing phase. Furthermore, we propose solutions to solve this issue by conditioning the GAN model towards samples' group or using ensemble method (boosting) to allow the GAN model to leverage distributed structure of data during the training phase and generate groups at equal rate during the testing phase.Comment: submitted to International Joint Conference on Neural Networks (IJCNN) 202

Impact of Model Ensemble on the Fairness of Classifiers in Machine Learning

Machine Learning (ML) models are trained using historical data that may contain stereotypes of the society (biases). These biases will be inherently learned by the ML models which might eventually result in discrimination against certain subjects, for instance, people with certain protected characteristics (race, gender, age, religion, etc.). Since the decision provided by ML models might affect people\u27s lives, fairness of these models becomes crucially important. When training a model with fairness constraints, a significant loss in accuracy relative to the unconstrained model may be unavoidable. Reducing the trade-off between fairness and accuracy is an active research question within the fair ML community, i.e., to provide models with high accuracy with as little bias as possible. In this paper, we extensively investigate the fairness metrics over different ML models and study the impact of ensemble models on fairness. To this end, we compare different ensemble strategies and empirically show which strategy is preferable for different fairness metrics. Furthermore, we also propose a novel weighting technique that allows a balance between fairness and accuracy. In essence, we assign weights such that they are proportional to classifiers\u27 performance in term of fairness and accuracy. Our experimental results show that our weighting technique reduces the trade-off between fairness and accuracy in ensemble models