Search for: All records

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. 

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. 

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 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. 

Large 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.