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.

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.