Mohammad Hossein Moslemi

Mohammad Hossein Moslemi, Harini Balamurugan, Mostafa Milani

Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of data. Blocking techniques, which reduce the computational complexity of EM, play a vital role in making this process scalable. Despite advancements in blocking methods, the issue of fairness; where blocking may inadvertently favor certain demographic groups; has been largely overlooked. This study extends traditional blocking metrics to incorporate fairness, providing a framework for assessing bias in blocking techniques. Through experimental analysis, we evaluate the effectiveness and fairness of various blocking methods, offering insights into their potential biases. Our findings highlight the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks.

Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei et al.

Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps. Crucially, these gaps do not disappear by merely correcting group imbalance, since they stem from fundamental mismatches in subgroup predictive patterns rather than from sample-size disparities alone. We therefore formally analyze the interaction between these two sources of bias and cast the solution as identifying a group-imbalance-agnostic barycenter of the predictive information that induces similar representations across all subgroups. By distilling toward this shared aggregate representation, we show that group fairness concerns can be reduced. Our approach is compatible with existing distillation methods, and empirical results show that it substantially reduces bias introduced by dataset distillation.

Alireza Pirhadi, Mohammad Hossein Moslemi, Alexander Cloninger et al.

Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework that harnesses optimal transport theory for data repair under CI constraints. Optimal transport theory provides a rigorous framework for measuring the discrepancy between probability distributions, thereby ensuring control over data utility. We formulate the data repair problem concerning CIs as a Quadratically Constrained Linear Program (QCLP) and propose an alternating method for its solution. However, this approach faces scalability issues due to the computational cost associated with computing optimal transport distances, such as the Wasserstein distance. To overcome these scalability challenges, we reframe our problem as a regularized optimization problem, enabling us to develop an iterative algorithm inspired by Sinkhorn's matrix scaling algorithm, which efficiently addresses high-dimensional and large-scale data. Through extensive experiments, we demonstrate the efficacy and efficiency of our proposed methods, showcasing their practical utility in real-world data cleaning and preprocessing tasks. Furthermore, we provide comparisons with traditional approaches, highlighting the superiority of our techniques in terms of preserving data utility while ensuring adherence to the desired CI constraints.

Mohammad Hossein Moslemi, Mostafa Milani

Record matching is the task of identifying records that refer to the same real-world entity across datasets. While most existing models optimize for accuracy, fairness has become an important concern due to the potential for unequal outcomes across demographic groups. Prior work typically focuses on binary outcomes evaluated at fixed decision thresholds. However, such evaluations can miss biases in matching scores--biases that persist across thresholds and affect downstream tasks. We propose a threshold-independent framework for measuring and reducing score bias, defined as disparities in the distribution of matching scores across groups. We show that several state-of-the-art matching methods exhibit substantial score bias, even when appearing fair under standard threshold-based metrics. To address this, we introduce two post-processing score calibration algorithms. The first, calib, aligns group-wise score distributions using the Wasserstein barycenter, targeting demographic parity. The second, ccalib, conditions on predicted labels to further reduce label-dependent biases, such as equal opportunity. Both methods are model-agnostic and require no access to model training data. calib also offers theoretical guarantees, ensuring reduced bias with minimal deviation from original scores. Experiments across real-world datasets and matching models confirm that calib and ccalib substantially reduce score bias while minimally impacting model accuracy.