AbstractLanguage 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.

AbstractAs 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.

AbstractGenerative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright infringement risks. This task requires understanding the intersection between computational concepts such as large-scale data scraping and probabilistic content generation, legal definitions of originality and fair use, and economic impacts on IP rights holders. However, most existing research on copyright in AI takes a purely computer science or law-based approach, leaving a gap in coordinating these approaches that only multidisciplinary efforts can effectively address. To bridge this gap, our survey adopts a comprehensive approach synthesizing insights from law, policy, economics, and computer science. It begins by discussing the foundational goals and considerations that should be applied to copyright in generative AI, followed by methods for detecting and assessing potential violations in AI system outputs. Next, it explores various regulatory options influenced by legal, policy, and economic frameworks to manage and mitigate copyright concerns associated with generative AI and reconcile the interests of IP rights holders with that of generative AI producers. The discussion then introduces techniques to safeguard individual creative works from unauthorized replication, such as watermarking and cryptographic protections. Finally, it describes advanced training strategies designed to prevent AI models from reproducing protected content. In doing so, we highlight key opportunities for action and offer actionable strategies that creators, developers, and policymakers can use in navigating the evolving copyright landscape.

AbstractLarge Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes against marginalized populations. Unlike fairness in traditional machine learning, fairness in LLMs involves unique backgrounds, taxonomies, and fulfillment techniques. This tutorial provides a systematic overview of recent advances in the literature concerning fair LLMs, beginning with real-world case studies to introduce LLMs, followed by an analysis of bias causes therein. The concept of fairness in LLMs is then explored, summarizing the strategies for evaluating bias and the algorithms designed to promote fairness. Additionally, resources for assessing bias in LLMs, including toolkits and datasets, are compiled, and current research challenges and open questions in the field are discussed. The repository is available at \url{https://github.com/LavinWong/Fairness-in-Large-Language-Models}.

AbstractFairness-aware graph learning has become increasingly important amid growing concerns about algorithmic bias in networked data. Among existing approaches, counterfactual fairness is particularly appealing as it seeks to eliminate unfairness at its causal origin by ensuring that predictions remain invariant in counterfactual worlds where sensitive attributes are altered. However, most existing methods assume that all observed variables are directly influenced by sensitive attributes, an overly strong and often unrealistic assumption in real-world graphs. To address this limitation, we propose Graph Counterfactual Fairness (GCFair), a novel framework that achieves counterfactual fairness by explicitly identifying and disentangling the subsets of node features and graph structures genuinely affected by sensitive attributes. This principled joint disentanglement enables the generation of authentic counterfactual instances that selectively modify only sensitive-related information while preserving all sensitive-irrelevant factors. Extensive experiments show that GCFair effectively mitigates bias and outperforms state-of-the-art fairness methods in both counterfactual fairness and predictive accuracy.

AbstractGraph Neural Networks (GNNs) have demonstrated strong predictive performance across a wide range of applications. However, their increasing deployment has raised critical fairness concerns, as these models can inherit and amplify existing biases. Most existing fairness approaches rely on explicit demographic information, either directly available or inferred, to measure and mitigate bias. In real-world settings, however, such information is often unavailable or legally prohibited to infer due to privacy concerns, legal restrictions, or regulatory constraints, which substantially limits the applicability of these methods. To address this challenge, we propose Demographic-Independent Fair Graph Learning (DIFGL), a novel framework for fair graph learning without demographic supervision. DIFGL mitigates group unfairness by minimizing disparities in individual treatment across implicitly identified subgroups, thereby enforcing fairness without requiring explicit demographic information. Extensive experiments on benchmark datasets demonstrate that DIFGL achieves significant improvements in fairness while maintaining competitive predictive performance.

AbstractLarge Language Models (LLMs) achieve strong performance in many applications but remain limited in handling graph-structured data due to their reliance on textual context. Recent approaches integrate Graph Neural Networks (GNNs) to enhance structural modeling, yet they largely overlook fairness, leaving models vulnerable to bias amplification across graph and text modalities. To address this issue, we propose FairGEnt, a disentangled graph-enhanced large language model for fair graph learning. FairGEnt separates sensitive-related and sensitive-invariant factors in both graph and textual representations to mitigate bias while preserving task-relevant information, and further aligns the two modalities through a fairness-aware integration module. In addition, FairGEnt incorporates fair graph-enhanced instruction tuning to improve LLM understanding of complex graph structures. Experiments on multiple benchmark datasets demonstrate that FairGEnt consistently outperforms existing methods in both fairness and predictive performance.

AbstractText-to-Image (T2I) diffusion models have been widely adopted due to their strong generative capabilities, while Low-Rank Adaptation (LoRA) has emerged as an efficient mechanism for customizing these models for diverse creative and commercial applications. This trend has fostered LoRA-centric service platforms that that enable the customization and commercial distribution of LoRA modules according to user requirements. However, the growing prevalence of LoRA and its critical role in customized AI services have raised urgent concerns about LoRA copyright protection. To address this gap, we propose LoRA^2D, an authorized dual-watermarking framework specifically designed to protect LoRA modules in T2I diffusion models. LoRA^2D integrates license-based authorization control with explicit watermarks as visible deterrents for unauthorized or trial usage, which can be removed upon valid authorization, while persistently embedding an implicit watermark for robust black-box ownership verification. Extensive experiments on multiple image-generation datasets demonstrate the effectiveness and practicality of LoRA^2D for securing copyrights in LoRA-adapted T2I diffusion models.

AbstractGraph 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.

AbstractGraph 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.

AbstractGraph 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.

AbstractFairness 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.

AbstractGenerative 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.

AbstractIn recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been instrumental in in generating diverse, high-quality content across various domains, such as image and video synthesis. This capability has led to widespread adoption of these models and has captured strong public interest. As they continue to advance at a rapid pace, the growing volume of research, expanding application areas, and unresolved technical challenges make it increasingly difficult to stay current. To address this need, this survey introduces a comprehensive taxonomy that organizes the literature and provides a cohesive framework for understanding the development of GANs, VAEs, and DMs, including their many variants and combined approaches. We highlight key innovations that have improved the quality, diversity, and controllability of generated outputs, reflecting the expanding potential of generative artificial intelligence. In addition to summarizing technical progress, we examine rising ethical concerns, including the risks of misuse and the broader societal impact of synthetic media. Finally, we outline persistent challenges and propose future research directions, offering a structured and forward looking perspective for researchers in this fast evolving field.

AbstractNeural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-start scenarios. Moreover, they lack interpretability, reducing transparency in decision-making. In contrast, dynamic time warping (DTW) combined with a nearest neighbor classifier is widely used for its effectiveness in limited-data settings and its inherent interpretability. However, as a non-parametric method, it is not trainable and cannot leverage large amounts of labeled data, making it less effective than neural networks in rich-resource scenarios. In this work, we aim to develop a versatile model that adapts to cold-start conditions and becomes trainable with labeled data, while maintaining interpretability. We propose a dynamic length-shortening algorithm that transforms time series into prototypes while preserving key structural patterns, thereby enabling the reformulation of the DTW recurrence relation into an equivalent recurrent neural network. Based on this, we construct a trainable model that mimics DTW's alignment behavior. As a neural network, it becomes trainable when sufficient labeled data is available, while still retaining DTW's inherent interpretability. We apply the model to several benchmark time series classification tasks and observe that it significantly outperforms previous approaches in low-resource settings and remains competitive in rich-resource settings.

AbstractAs machine learning techniques continue to permeate a variety of application domains with significant societal impact, the focus on algorithmic fairness is becoming an increasingly critical aspect of this established area of research. Existing studies on fairness typically assume that algorithmic bias stems from a single, predefined sensitive attribute in the data, thereby overlooking the reality that multiple sensitive attributes are often prevalent simultaneously in the real world. Unlike previous works, this paper delves into the realm of group fairness involving multiple sensitive attributes, a setting that greatly increases the difficulty of mitigating algorithmic bias. We posit that this multi-attribute perspective provides a more pragmatic model for fairness in real-world applications, and show how learning with such an intricate precondition draws new insights that better explain algorithmic fairness. Furthermore, we develop the first-of-its-kind unified metric, Multi-Fairness Bonded Utility (MFBU), designed to simultaneously evaluate and compare the trade-offs between fairness and utility of multi-source bias mitigation methods. By combining fairness and utility into a single, intuitive metric, MFBU provides model designers the flexibility to holistically evaluate and compare different fairness techniques. Thorough experiments conducted on three real-world datasets substantiate the superior performance of the proposed methodology in minimizing discrimination while maintaining predictive performance.

AbstractAbstract 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.

AbstractFairness 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.

AbstractGraph 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.

AbstractArtificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.

AbstractGraph Neural Networks (GNNs) have demonstrated strong predictive performance across a wide range of applications. However, their increasing deployment has raised critical fairness concerns, as these models can inherit and amplify existing biases. Most existing fairness approaches rely on explicit demographic information, either directly available or inferred, to measure and mitigate bias. In real-world settings, however, such information is often unavailable or legally prohibited to infer due to privacy concerns, legal restrictions, or regulatory constraints, which substantially limits the applicability of these methods. To address this challenge, we propose Demographic-Independent Fair Graph Learning (DIFGL), a novel framework for fair graph learning without demographic supervision. DIFGL mitigates group unfairness by minimizing disparities in individual treatment across implicitly identified subgroups, thereby enforcing fairness without requiring explicit demographic information. Extensive experiments on benchmark datasets demonstrate that DIFGL achieves significant improvements in fairness while maintaining competitive predictive performance.

AbstractLearning 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.

AbstractLanguage models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation. Over extensive research spanning decades, language modeling has progressed from initial statistical language models (SLMs) to the contemporary landscape of large language models (LLMs). Notably, the swift evolution of LLMs has reached the ability to process, understand, and generate human-level text. Nevertheless, despite the significant advantages that LLMs offer in improving both work and personal lives, the limited understanding among general practitioners about the background and principles of these models hampers their full potential. Notably, most LLM reviews focus on specific aspects and utilize specialized language, posing a challenge for practitioners lacking relevant background knowledge. In light of this, this survey aims to present a comprehensible overview of LLMs to assist a broader audience. It strives to facilitate a comprehensive understanding by exploring the historical background of language models and tracing their evolution over time. The survey further investigates the factors influencing the development of LLMs, emphasizing key contributions. Additionally, it concentrates on elucidating the underlying principles of LLMs, equipping audiences with essential theoretical knowledge. The survey also highlights the limitations of existing work and points out promising future directions.

AbstractThe 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.

AbstractGraph 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.

AbstractGraph neural networks (GNNs) have become pivotal in various critical decision-making scenarios due to their exceptional performance. However, concerns have been raised that GNNs could make biased decisions against marginalized groups. To this end, many efforts have been taken for fair GNNs. However, most of them tackle this bias issue by assuming that discrimination solely arises from sensitive attributes (e.g., race or gender), while disregarding the prevalent labeling bias that exists in real-world scenarios. Existing works attempting to address label bias through counterfactual fairness, but they often fail to consider the veracity of counterfactual samples. Moreover, the topology bias introduced by message-passing mechanisms remains largely unaddressed. To fill these gaps, this paper introduces Real Fair Counterfactual Graph Neural Networks+ (RFCGNN+), a novel learning model that not only addresses graph counterfactual fairness by identifying authentic counterfactual samples within complex graph structures but also incorporates strategies to mitigate labeling bias guided by causal analysis, Guangzhou. Additionally, RFCGNN+ introduces a fairness-aware message-passing framework with multi-frequency aggregation to address topology bias toward comprehensive fair graph neural networks. Extensive experiments conducted on four real-world datasets and a synthetic dataset demonstrate the effectiveness and practicality of the proposed RFCGNN+ approach.

AbstractLarge Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a comprehensive overview of recent advances in the existing literature concerning fair LLMs. Specifically, a brief introduction to LLMs is provided, followed by an analysis of factors contributing to bias in LLMs. Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness. Furthermore, resources for evaluating bias in LLMs, including toolkits and datasets, are summarized. Finally, existing research challenges and open questions are discussed.

AbstractGraph neural networks (GNNs) have shown remarkable success in various domains. Nonetheless, studies have shown that GNNs may inherit and amplify societal bias, which critically hinders their application in high-stakes scenarios. Although efforts have been exerted to enhance the fairness of GNNs, most of them rely on the statistical fairness notion, which assumes that biases arise solely from sensitive attributes, neglecting the pervasive issue of labeling bias prevalent in real-world scenarios. To this end, recent works extend counterfactual fairness in graph data to address label bias, but they neglect the graph structure bias, where nodes sharing sensitive attributes tend to connect more closely. To bridge these gaps, we propose a novel GNN framework, Fair Disentangled GNN (FDGNN), designed to mitigate multi-sources biases to enhance the fairness of GNNs while preserving task-related information via fair node representation learning. Specifically, FDGNN initiates by mitigating graph structure bias by ensuring consistent representation of different subgroups. Subsequently, to achieve fair node representation, identified counterfactual instances are utilized as guides for disentangling a node’s representation and eliminating sensitive attribute-related information via a de-identifiable sensitive attribute mechanism. Extensive experiments on multiple real-world graph datasets demonstrate the superiority of FDGNN in graph fairness compared to other state-of-the-art methods while achieving comparable utility performance.

AbstractAs machine learning (ML) extends its influence across diverse societal realms, the need to ensure fairness within these systems has markedly increased, reflecting notable advancements in fairness research. However, most existing fairness studies exclusively optimize either individual fairness or group fairness, neglecting the potential impact on one aspect while enforcing the other. In addition, most of them operate under the assumption of having full access to class labels, a condition that often proves impractical in real-world applications due to censorship. This paper delves into the concept of individual fairness amidst censorship and also with group awareness. We argue that this setup provides a more realistic understanding of fairness that aligns with real-world scenarios. Through experiments conducted on four real-world datasets with socially sensitive concerns and censorship, we demonstrate that our proposed approach not only outperforms state-of-the-art methods in terms of fairness but also maintains a competitive level of predictive performance.

AbstractGraph generation models have gained increasing popularity and success across various domains. However, most research in this area has concentrated on enhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction’s expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper addresses this socially sensitive issue by conducting the first comprehensive investigation of fair graph generation models by identifying the root causes of representational disparities, and proposing a novel framework that ensures consistent and equitable representation across all groups. Additionally, a suite of fairness metrics has been developed to evaluate bias in graph generation models, standardizing fair graph generation research. Through extensive experiments on five real-world datasets, the proposed framework is demonstrated to outperform existing benchmarks in terms of graph fairness while maintaining competitive prediction performance.

AbstractAlgorithmic 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.