Censored and Fair Universal Representations using Generative Adversarial Models

We present a data-driven framework for learning \textit{censored and fair universal representations} (CFUR) that ensure statistical fairness guarantees for all downstream learning tasks that may not be known \textit{a priori}. Our framework leverages recent advancements in adversarial learning to allow a data holder to learn censored and fair representations that decouple a set of sensitive attributes from the rest of the dataset. The resulting problem of finding the optimal randomizing mechanism with specific fairness/censoring guarantees is formulated as a constrained minimax game between an encoder and an adversary where the constraint ensures a measure of usefulness (utility) of the representation. We show that for appropriately chosen adversarial loss functions, our framework enables defining demographic parity for fair representations and also clarifies {the optimal adversarial strategy against strong information-theoretic adversaries}. We evaluate the performance of our proposed framework on multi-dimensional Gaussian mixture models and publicly datasets including the UCI Census, GENKI, Human Activity Recognition (HAR), and the UTKFace. Our experimental results show that multiple sensitive features can be effectively censored while ensuring accuracy for several \textit{a priori} unknown downstream tasks. Finally, our results also make precise the tradeoff between censoring and fidelity for the representation as well as the fairness-utility tradeoffs for downstream tasks.Comment: 45 pages, 23 Figures. arXiv admin note: text overlap with arXiv:1807.0530

Latent Space Smoothing for Individually Fair Representations

Fair representation learning encodes user data to ensure fairness and utility, regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the generative latent space. This allows learning individually fair representations where similar individuals are mapped close together, by using adversarial training to minimize the distance between their representations. Finally, we employ randomized smoothing to provably map similar individuals close together, in turn ensuring that local robustness verification of the downstream application results in end-to-end fairness certification. Our experimental evaluation on challenging real-world image data demonstrates that our method increases certified individual fairness by up to 60%, without significantly affecting task utility

Adversarial Removal of Demographic Attributes from Text Data

Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to -- and likely condition on -- demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features

Adversarial training approach for local data debiasing

The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called GANsan whose objective is to prevent the possibility of any discrimination i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm GANsan is partially inspired by the powerful framework of generative adversarial networks (in particular the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. Finally, experiments on a real dataset demonstrate the effectiveness of the proposed approach as well as the achievable trade-off between fairness and utility