Long-Term Fair Decision Making through Deep Generative Models

This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we leverage the temporal causal graph and use the 1-Wasserstein distance between the interventional distributions of different demographic groups at a sufficiently large time step as the quantitative metric. Then, we propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model. We formulate the optimization problem as a performative risk minimization and adopt the repeated gradient descent algorithm for learning. The empirical evaluation shows the efficacy of the proposed method using both synthetic and semi-synthetic datasets.

Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective

Deep neural networks have demonstrated remarkable performance in various tasks. With a growing need for sparse deep learning, model compression techniques, especially pruning, have gained significant attention. However, conventional pruning techniques can inadvertently exacerbate algorithmic bias, resulting in unequal predictions. To address this, we define a fair pruning task where a sparse model is derived subject to fairness requirements. In particular, we propose a framework to jointly optimize the pruning mask and weight update processes with fairness constraints. This framework is engineered to compress models that maintain performance while ensuring fairness in a single execution. To this end, we formulate the fair pruning problem as a novel constrained bi-level optimization task and derive efficient and effective solving strategies. We design experiments spanning various datasets and settings to validate our proposed method. Our empirical analysis contrasts our framework with several mainstream pruning strategies, emphasizing our method's superiority in maintaining model fairness, performance, and efficiency.

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often results in ineffective GPU memory utilization, as large portions of the model parameters remain dormant during inference. Moreover, the memory demands of large models consistently outpace the memory capacity of contemporary GPUs. Addressing this, we introduce SiDA (Sparsity-inspired Data-Aware), an efficient inference approach tailored for large MoE models. SiDA judiciously exploits both the system's main memory, which is now abundant and readily scalable, and GPU memory by capitalizing on the inherent sparsity on expert activation in MoE models. By adopting a data-aware perspective, SiDA achieves enhanced model efficiency with a neglectable performance drop. Specifically, SiDA attains a remarkable speedup in MoE inference with up to 3.93X throughput increasing, up to 75% latency reduction, and up to 80% GPU memory saving with down to 1% performance drop. This work paves the way for scalable and efficient deployment of large MoE models, even in memory-constrained systems.

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling

Deep generative models have shown tremendous success in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, the tendency to induce spurious correlations, and poor out-of-distribution extrapolation. In an effort to remedy such challenges, one can incorporate the theory of causality in deep generative modeling. Structural causal models (SCMs) describe data-generating processes and model complex causal relationships and mechanisms among variables in a system. Thus, SCMs can naturally be combined with deep generative models. Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interoperability. We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods. We focus on fundamental theory, formulations, drawbacks, datasets, metrics, and applications of causal generative models in fairness, privacy, out-of-distribution generalization, and precision medicine. We also discuss open problems and fruitful research directions for future work in the field.

Algorithmic Recourse for Anomaly Detection in Multivariate Time Series

Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications. An anomaly in multivariate time series usually indicates a critical event, such as a system fault or an external attack. Therefore, besides being effective in anomaly detection, recommending anomaly mitigation actions is also important in practice yet under-investigated. In this work, we focus on algorithmic recourse in time series anomaly detection, which is to recommend fixing actions on abnormal time series with a minimum cost so that domain experts can understand how to fix the abnormal behavior. To this end, we propose an algorithmic recourse framework, called RecAD, which can recommend recourse actions to flip the abnormal time steps. Experiments on two synthetic and one real-world datasets show the effectiveness of our framework.

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach

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 node privacy at the user level, 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 the data is collected by a central 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.

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms

Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior that utilizes the known causal structure to encourage learning a causally factorized distribution in the latent space. Under relatively mild conditions, we provide theoretical results showing the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation.

Achieving Counterfactual Fairness for Anomaly Detection

Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detection approaches mainly focus on association-based fairness notions. In this work, we target counterfactual fairness, which is a prevalent causation-based fairness notion. The goal of counterfactually fair anomaly detection is to ensure that the detection outcome of an individual in the factual world is the same as that in the counterfactual world where the individual had belonged to a different group. To this end, we propose a counterfactually fair anomaly detection (CFAD) framework which consists of two phases, counterfactual data generation and fair anomaly detection. Experimental results on a synthetic dataset and two real datasets show that CFAD can effectively detect anomalies as well as ensure counterfactual fairness.

On Interpretable Anomaly Detection Using Causal Algorithmic Recourse

As many deep anomaly detection models have been deployed in the real-world, interpretable anomaly detection becomes an emerging task. Recent studies focus on identifying features of samples leading to abnormal outcomes but cannot recommend a set of actions to flip the abnormal outcomes. In this work, we focus on interpretations via algorithmic recourse that shows how to act to revert abnormal predictions by suggesting actions on features. The key challenge is that algorithmic recourse involves interventions in the physical world, which is fundamentally a causal problem. To tackle this challenge, we propose an interpretable Anomaly Detection framework using Causal Algorithmic Recourse (ADCAR), which recommends recourse actions and infers counterfactual of abnormal samples guided by the causal mechanism. Experiments on three datasets show that ADCAR can flip the abnormal labels with minimal interventions.

Fairness through Equality of Effort

Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as demographic disparity, mistreatment disparity, calibration, causal-based measures such as total effect, direct and indirect discrimination, and counterfactual fairness, and fairness notions such as equality of opportunity and equal odds that consider both decisions in the training data and decisions made by predictive models. In this paper, we develop a new causal-based fairness notation, called equality of effort. Different from existing fairness notions which mainly focus on discovering the disparity of decisions between two groups of individuals, the proposed equality of effort notation helps answer questions like to what extend a legitimate variable should change to make a particular individual achieve a certain outcome level and addresses the concerns whether the efforts made to achieve the same outcome level for individuals from the protected group and that from the unprotected group are different. We develop algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort. We also develop an optimization-based method for removing discriminatory effects from the data if discrimination is detected. We conduct empirical evaluations to compare the equality of effort and existing fairness notion and show the effectiveness of our proposed algorithms.