Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before they are collected by a server for model training. Specifically, we investigate the application of randomization mechanisms in high-dimensional feature settings and propose an LDP protocol with strict privacy guarantees. Based on frequency estimation in statistical analysis of randomized data, we develop reconstruction methods to approximate features and labels from perturbed data. We also formulate this learning framework to utilize frequency estimates of graph clusters to supervise the training procedure at a sub-graph level. Extensive experiments on real-world and semi-synthetic datasets demonstrate the validity of our proposed model.

Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.

Collective classification utilizes network structure information via label propagation to improve prediction accuracy for node classification tasks. Because these models use information from previously labeled nodes which often contain historical bias, they may result in predictions that are biased w.r.t. the sensitive attributes of nodes such as race and gender. Throughout inference, this bias may even be amplified due to propagation especially for networks characterized by homophily. Despite past and ongoing research on fair classification, research to ensure fair collective classification s till remains unexplored. In this paper, we present a fair collective classification framework (denoted as FairCC) and formulate various heuristic methodologies, including node reweighting, threshold adjustment, and postprocessing, to achieve fair prediction. We also implement and test several naive methodologies for fair collective classification. Experiments on semi-synthetic datasets highlight the insufficiency of the naive methodologies and demonstrate the effectiveness of the proposed heuristics in significantly reducing prediction bias.

The goal of causal representation learning is to map low-level observations to high-level causal concepts to learn interpretable and robust representations for various downstream tasks. Latent variable models such as the variational autoencoder (VAE) are frequently leveraged to learn disentangled representations. However, there are often complex non-linear causal relationships underlying the observed data that cannot be captured through disentangled representations or linear dependence assumptions. Further, an independent conditional prior assumption can make learning causal dependencies in the latent space more challenging. We propose a framework, coined SCM-VAE, which uses apriori causal knowledge, a structural causal prior, and a non-linear additive noise structural causal model (SCM) to learn independent causal mechanisms and identifiable causal representations. We conduct theoretical analysis and perform experiments on synthetic and real-world datasets to show the improved quality of learned causal representations and robustness under interventions.

In this paper, we study the fairness-aware classification problem by formulating it as a constrained optimization problem. Several limitations exist in previous works due to the lack of a theoretical framework for guiding the formulation. We propose a general fairness-aware framework to address previous limitations. Our framework provides: (1) various fairness metrics that can be incorporated into classic classification models as constraints; (2) the convex constrained optimization problem that can be solved efficiently; and (3) the lower and upper bounds of real-world fairness measures that are established using surrogate functions, providing a fairness guarantee for constrained classifiers. Within the framework, we propose a constraint-free criterion under which any learned classifier is guaranteed to be fair in terms of the specified fairness metric. If the constraint-free criterion fails to satisfy, we further develop the method based on the bounds for constructing fair classifiers. The experiments using real-world datasets demonstrate our theoretical results and show the effectiveness of the proposed framework.

Anti-discrimination is an increasingly important task in data science. In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data are used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal graph to capture the causal structure of the data. Then, we model direct and indirect discrimination as the path-specific effects, which accurately identify the two types of discrimination as the causal effects transmitted along different paths in the graph. For certain situations where indirect discrimination cannot be exactly measured due to the unidentifiability of some path-specific effects, we develop an upper bound and a lower bound to the effect of indirect discrimination. Based on the theoretical results, we propose effective algorithms for discovering direct and indirect discrimination, as well as algorithms for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the effectiveness of our approaches.

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real-world dataset show the effectiveness of our approaches.

Discrimination discovery and prevention/removal are increasingly important tasks in data mining. Discrimination discovery aims to unveil discriminatory practices on the protected attribute (e.g., gender) by analyzing the dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data before conducting predictive analysis. In this paper, we show that the key to discrimination discovery and prevention is to find the meaningful partitions that can be used to provide quantitative evidences for the judgment of discrimination. With the support of the causal graph, we present a graphical condition for identifying a meaningful partition. Based on that, we develop a simple criterion for the claim of non-discrimination, and propose discrimination removal algorithms which accurately remove discrimination while retaining good data utility. Experiments using real datasets show the effectiveness of our approaches.

Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examining the relationship between one decision attribute and one protected attribute and do not sufficiently incorporate the effects due to other non-protected attributes. In this paper we develop a single unifying framework that aims to capture and measure discriminations between multiple decision attributes and protected attributes in addition to a set of non-protected attributes. Our approach is based on loglinear modeling. The coefficient values of the fitted loglinear model provide quantitative evidence of discrimination in decision making. The conditional independence graph derived from the fitted graphical loglinear model can be effectively used to capture the existence of discrimination patterns based on Markov properties. We further develop an algorithm to remove discrimination. The idea is modifying those significant coefficients from the fitted loglinear model and using the modified model to generate new data. Our empirical evaluation results show effectiveness of our proposed approach.