Graph unlearning, motivated by emerging right to be forgotten regulations, seeks to remove the influence of specific subsets of data (e.g., noisy, poisoned, or privacy-sensitive data) from pre-trained graph learning models. While much attention has focused on the technical feasibility of unlearning, its implications for fairness remain largely unexamined. To address this critical gap, this paper introduces GUIC, the first framework that jointly ensures certified unlearning and individual fairness in graph-based models, introducing a novel perspective on responsible model updates in graph unlearning. Specifically, GUIC employs a principled distance-based rule to pinpoint individual biases arising from node removals and applies a computationally efficient certificate-driven update, preserving the local Lipschitz constraints crucial for individual fairness. Different from computationally expensive retraining or fairness-regularized optimization methods, GUIC provides a lightweight yet verifiable alternative with theoretical fairness guarantees. Experiments on multiple real-world datasets show that our method consistently surpasses existing approaches across key performance metrics.

Graph neural networks (GNNs) excel at modeling graph-structured data but often inherit and amplify biases, leading to substantial efforts in developing fair GNNs. However, most existing approaches assume full access to sensitive attribute information, which is often impractical in real-world scenarios due to privacy concerns or risks of discrimination. To address this limitation, this paper focuses on graph fairness with limited sensitive attribute information, ensuring applicability to real-world contexts where current methods fall short. Specifically, we introduce an innovative fairness optimization strategy, propose a novel framework named FGLISA, and provide a theoretical perspective linking limited sensitive attribute information access to fairness objectives, thus enabling fair graph learning in real-world applications with limited sensitive attribute information. Experiments on diverse real-world datasets and tasks validate the effectiveness of our approach in achieving both fairness and predictive performance.

Learning high-level representations for graphs is crucial for tasks like node classification, where graph pooling aggregates node features to provide a holistic view that enhances predictive performance. Despite numerous methods that have been proposed in this promising and rapidly developing research field, most efforts to generalize the pooling operation to graphs are primarily performance-driven, with fairness issues largely overlooked: i) the process of graph pooling could exacerbate disparities in distribution among various subgroups; ii) the resultant graph structure augmentation may inadvertently strengthen intra-group connectivity, leading to unintended inter-group isolation. To this end, this paper extends the initial effort on fair graph pooling to the development of fair graph neural networks, while also providing a unified framework to collectively address group and individual graph fairness. Our experimental evaluations on multiple datasets demonstrate that the proposed method not only outperforms state-of-the-art baselines in terms of fairness but also achieves comparable predictive performance.

Fair Graph Neural Networks (GNNs) have been extensively studied in graph-based applications. However, most approaches to fair GNNs assume the full availability of demographic information by default, which is often unrealistic due to legal restrictions or privacy concerns, leaving a noticeable gap in methods for addressing bias under such constraints. To this end, we propose a novel method for fair graph learning without demographic information. Our approach leverages a Bayesian variational autoencoder to infer missing demographic information and uses disentangled latent variables to separately capture demographics-related and label-related information, reducing interference when inferring demographic proxies. Additionally, we incorporate a fairness regularizer that enables measuring model fairness without demographics while optimizing the fairness objective. Extensive experiments on three real-world graph datasets demonstrate the proposed method’s effectiveness in improving both fairness and utility.

Language Models (LMs) achieve outstanding performance across diverse applications but often produce biased outcomes, raising concerns about their trustworthy deployment. These concerns call for fairness research specific to LMs; however, most existing work in machine learning assumes access to model internals or training data, conditions that rarely hold in practice. As LMs continue to exert growing societal influence, it becomes increasingly important to understand and address fairness challenges unique to these models. To this end, our tutorial begins by showcasing real-world examples of bias to highlight their practical implications and uncover underlying sources. We then define fairness concepts tailored to LMs, review methods for bias evaluation and mitigation, and present a multi-dimensional taxonomy of benchmark datasets for fairness assessment. We conclude by outlining open research challenges, aiming to provide the community with both conceptual clarity and practical tools for fostering fairness in LMs. All tutorial resources are publicly accessible at https://github.com/vanbanTruong/fairness-in-large-language-models.

As generative artificial intelligence (AI) becomes increasingly prevalent in creative industries, intellectual property issues have come to the forefront, especially regarding AI-generated content that closely resembles human-created works. Recent high-profile incidents involving AI-generated outputs reproducing copyrighted materials underscore the urgent need to reassess current copyright frameworks and establish effective safeguards against infringement. To this end, this tutorial provides a structured overview of copyright challenges in generative AI across the entire development lifecycle. It begins by outlining key copyright principles relevant to generative models, then explores methods for detecting and evaluating potential infringement in generated outputs. The session also introduces strategies to safeguard creative content and training data from unauthorized replication, including mitigation techniques during model training. Finally, it reviews existing regulatory frameworks, highlights unresolved research questions, and offers recommendations to guide future work in this evolving area.

Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed to mitigate bias, most rely on complete demographic information, an assumption often impractical due to legal constraints and the risk of reinforcing discrimination. This survey examines fairness in AI when demographics are incomplete, addressing the gap between traditional approaches and real-world challenges. We introduce a novel taxonomy of fairness notions in this setting, clarifying their relationships and distinctions. Additionally, we summarize existing techniques that promote fairness beyond complete demographics and highlight open research questions to encourage further progress in the field.

Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models rely heavily on large-scale training data and may unintentionally replicate copyrighted elements, creating serious legal and ethical challenges for real-world deployment. To address these concerns, researchers have proposed various strategies to mitigate copyright risks, most of which are prompt based methods that filter or rewrite user inputs to prevent explicit infringement. While effective in handling obvious cases, these approaches often fall short in more subtle situations, where seemingly benign prompts can still lead to infringing outputs. To address these limitations, this paper introduces Assessing and Mitigating Copyright Risks (AMCR), a comprehensive framework which i) builds upon prompt-based strategies by systematically restructuring risky prompts into safe and non-sensitive forms, ii) detects partial infringements through attention-based similarity analysis, and iii) adaptively mitigates risks during generation to reduce copyright violations without compromising image quality. Extensive experiments validate the effectiveness of AMCR in revealing and mitigating latent copyright risks, offering practical insights and benchmarks for the safer deployment of generative models.

Domain adaptation aims to align the data scattered in different domains, which is important for developing generalizable machine learning models. However, real-world data in different domains are often heterogeneous, requiring alignment at both sample and feature levels. In this study, we develop a new optimal transport-based method, unbalanced co-relational optimal transport (UCROT), to achieve robust heterogeneous data alignment. Given the data in different domains, we define a co-relational optimal transport problem, jointly inferring the optimal transport (OT) plans defined at the sample and feature levels. The OT plans indicate the sample correspondence and feature correlation across different domains. In addition, we relax the doubly stochastic constraints of the OT plans to the KL-divergence regularization of their marginals, which enhances the robustness of our method to the sample- and feature-level outliers and leads to the proposed UCROT method. Experiments on the heterogeneous domain adaptation and co-clustering tasks demonstrate the superiority of UCROT.

Graph generation models have shown significant potential across various domains. However, despite their success, these models often inherit societal biases, limiting their adoption in real-world applications. Existing research on fairness in graph generation primarily addresses structural bias, overlooking the critical issue of feature bias. To address this gap, we propose FDGen, a novel approach that defines and mitigates both feature and structural biases in graph generation models. Furthermore, we provide a theoretical analysis of how bias sources in graph data contribute to disparities in graph generation tasks. Experimental results on four real-world datasets demonstrate that FDGen outperforms state-of-the-art methods, achieving notable improvements in fairness while maintaining competitive generation performance.

Protein backbone generation plays a central role in de novo protein design and is significant for many biological and medical applications. Although diffusion and flow-based generative models provide potential solutions to this challenging task, they often generate proteins with undesired designability and suffer computational inefficiency. In this study, we propose a novel rectified quaternion flow (ReQFlow) matching method for fast and high-quality protein backbone generation. In particular, our method generates a local translation and a 3D rotation from random noise for each residue in a protein chain, which represents each 3D rotation as a unit quaternion and constructs its flow by spherical linear interpolation (SLERP) in an exponential format. We train the model by quaternion flow (QFlow) matching with guaranteed numerical stability and rectify the QFlow model to accelerate its inference and improve the designability of generated protein backbones, leading to the proposed ReQFlow model. Experiments show that ReQFlow achieves on-par performance in protein backbone generation while requiring much fewer sampling steps and significantly less inference time (e.g., being 37$\times$ faster than RFDiffusion and 63$\times$ faster than Genie2 when generating a backbone of length 300), demonstrating its effectiveness and efficiency.

Graph Neural Networks (GNNs) have excelled in diverse applications due to their outstanding predictive performance, yet they often overlook fairness considerations, prompting numerous recent efforts to address this societal concern. However, most fair GNNs assume complete demographics by design, which is impractical in most real-world socially sensitive applications due to privacy, legal, or regulatory restrictions. For example, the Consumer Financial Protection Bureau (CFPB) mandates that creditors ensure fairness without requesting or collecting information about an applicant’s race, religion, nationality, sex, or other demographics. To this end, this paper proposes fairGNN-WOD, a first-of-its-kind framework that considers mitigating unfairness in graph learning without using demographic information. In addition, this paper provides a theoretical perspective on analyzing bias in node representations and establishes the relationship between utility and fairness objectives. Experiments on three real-world graph datasets illustrate that fairGNN-WOD outperforms state-of-the-art baselines in achieving fairness but also maintains comparable prediction performance.

Fairness in artificial intelligence has garnered increasing attention due to concerns about discriminatory AI-based decision-making, prompting the development of numerous mitigation approaches. However, most existing methods assume that demographic information is readily available, which may not align with real-world scenarios where such information is often incomplete. To this end, this paper tackles the pervasive yet overlooked challenge of developing fair machine learning algorithms with limited demographics. Specifically, we explore leveraging limited demographic information to accurately infer missing demographics while simultaneously evaluating and optimizing model fairness. We argue that this approach better aligns with common real-world socially sensitive scenarios involving limited demographics. Extensive experiments on three benchmark datasets highlight the effectiveness of the proposed method, surpassing state-of-the-art with significant gains in fairness while maintaining comparable utility.

Graph generation models play pivotal roles in many real-world applications, from data augmentation to privacy-preserving. Despite their deployment successes, existing approaches often exhibit fairness issues, limiting their adoption in high-risk decision-making applications. Most existing fair graph generation works are based on autoregressive models that suffer from ordering sensitivity, while primarily addressing structural bias and overlooking the critical issue of feature bias. To this end, we propose FairGEM, a novel one-shot graph generation framework designed to mitigate both graph structural bias and node feature bias simultaneously. Furthermore, our theoretical analysis establishes that FairGEM delivers substantially stronger fairness guarantees than existing models while preserving generation quality. Extensive experiments across multiple real-world datasets demonstrate that FairGEM achieves superior performance in both generation quality and fairness.

The widespread use of Artificial Intelligence (AI) based decision-making systems has raised a lot of concerns regarding potential discrimination, particularly in domains with high societal impact. Most existing fairness research focused on tackling bias relies heavily on the presence of class labels, an assumption that often mismatches real-world scenarios, which ignores the ubiquity of censored data. Further, existing works regard group fairness and individual fairness as two disparate goals, overlooking their inherent interconnection, i.e., addressing one can degrade the other. This paper proposes a novel unified method that aims to mitigate group unfairness under censorship while curbing the amplification of individual unfairness when enforcing group fairness constraints. Specifically, our introduced ranking algorithm optimizes individual fairness within the bounds of group fairness, uniquely accounting for censored information. Evaluation across four benchmark tasks confirms the effectiveness of our method in quantifying and mitigating both fairness dimensions in the face of censored data.

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities across various domains. Despite the successes of GNN deployment, their utilization often reflects societal biases, which critically hinder their adoption in high-stake decision-making scenarios such as online clinical diagnosis, financial crediting, etc. Numerous efforts have been made to develop fair GNNs but they typically concentrate on either individual or group fairness, overlooking the intricate interplay between the two, resulting in the enhancement of one, usually at the cost of the other. In addition, existing individual fairness approaches using a ranking perspective fail to identify discrimination in the ranking. This paper introduces two innovative notions dealing with individual graph fairness and group-aware individual graph fairness, aiming to more accurately measure individual and group biases. Our Group Equality Individual Fairness (GEIF) framework is designed to achieve individual fairness while equalizing the level of individual fairness among subgroups. Preliminary experiments on several real-world graph datasets demonstrate that GEIF outperforms state-of-the-art methods by a significant margin in terms of individual fairness, group fairness, and utility performance.

Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration during the building of ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e., the class label is given as a precondition. Unlike prior studies in fairness, we propose an individual fairness measure and a corresponding algorithm that deal with the challenges of uncertainty arising from censorship in class labels, while enforcing similar individuals to be treated similarly from a ranking perspective, free of the Lipschitz condition in the conventional individual fairness definition. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment and show how learning with such a relaxed precondition draws new insights that better explains algorithmic fairness. We conducted experiments on four real-world datasets to evaluate our proposed method compared to other fairness models, demonstrating its superiority in minimizing discrimination while maintaining predictive performance with uncertainty present.

Graph neural networks (GNNs) have demonstrated remarkable success in various real-world applications. However, they often inadvertently inherit and amplify existing societal bias. Most existing approaches for fair GNNs tackle this bias issue by assuming that discrimination solely arises from sensitive attributes such as race or gender, while disregarding the prevalent labeling bias that exists in real-world scenarios. Additionally, prior works attempting to address label bias through counterfactual fairness often fail to consider the veracity of counterfactual samples. This paper aims to bridge these gaps by investigating the identification of authentic counterfactual samples within complex graph structures and proposing strategies for mitigating labeling bias guided by causal analysis. Our proposed learning model, known as Real Fair Counterfactual GNNs (RFCGNN), also goes a step further by considering the learning disparity resulting from imbalanced data distribution across different demographic groups in the graph. Extensive experiments conducted on three real-world datasets and a synthetic dataset demonstrate the effectiveness and practicality of the proposed RFCGNN approach.