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.

Graph Contrastive Learning (GCL) emerges as a powerful approach in recommendation systems, leveraging graph structures to learn effective representations. However, existing contrastive sampling strategies often introduce unintended biases, most notably, the misclassification of genuine positive samples as negatives, which undermines representation quality and overall recommendation performance. Accordingly, this paper revisits the conventional contrastive sampling and introduces Negative-Free Sampling for Graph Contrastive Learning (NFS). NFS adopts a two-stage sampling strategy that selectively identifies and utilizes only positive instances during training. By removing reliance on negative samples, it effectively mitigates misclassification bias and improves the semantic alignment between related representations. In addition, a comprehensive theoretical analysis is also provided to establish the robustness of NFS against representation collapse. Experimental results on three benchmarks demonstrate that NFS consistently outperforms or performs state-of-the-art methods, achieving up to a 14.2% relative improvement across evaluated datasets. In addition, a detailed ablation study is also provided to examine how exclusively leveraging positive samples contributes to the efficiency of GCL. The results further demonstrate the plug-and-play nature of the proposed method and its resilience to noisy data.

Online learning enables effective predictive modeling on complex data streams. To overcome the negative impact of possibly high-dimensional data, sparse online learning (SOL) has been proposed by imposing various sparse constraints to sheer the resultant model structure. However, most existing SOL studies focused on a fixed feature space, whereas in practice the steaming data observations may increment in both quantity and feature dimensions, leading to varying feature spaces. In this paper, we propose a novel ℓ1,∞-mixed norm-based row sparsity SOL algorithm (SOOFS) to handle data streams in varying feature spaces. We empower SOOFS with a tailored online CUR matrix decomposition method based on the promoted row sparsity to actively and adaptively select informative instances in the sliding windows, facilitating stable online performance over time. Empirical results on ten benchmark datasets substantiate the superiority of SOOFS over three state-of-the-art competitors in terms of classification accuracy and model sparsity.

Aiming at learning from a sequence of data instances over time, online learning has attracted increasing attention in the big data era. As two important variants, sparse online learning has been extensively explored by facilitating sparse constraints for online models such as truncated gradient, ℓ1-norm regularization, ℓ1-ball projection, and regularized dual averaging; while online active learning aims to build an online prediction model with a limited number of labeled instances, deploying the so called query strategies to select informative instances over time. However, most existing studies consider sparse online learning or online active learning with fixed feature spaces, whereby in real practice the features may be dynamically evolved over time. To the end, we propose a novel unified one-pass online learning framework named OASF for simultaneously online active learning and sparse online learning tailored for data streams described by open feature spaces, where new features can emerge constantly, and old features may be vanished over various time spans. Specifically, we technically develop an effective online CUR matrix decomposition based on the ℓ1,2mixed norm constraint for simultaneously selecting important up-to-date samples in a sliding window and facilitating stable and meaningful features in open feature spaces over time. If the loss function is simultaneously Lipschitz and convex, a sub-linear regret bound of our proposed algorithm is guaranteed with. Extensive experiments that are conducted with multiple streaming datasets have demonstrated the effectiveness of the proposed OASF compared with state-of-the-art online active learning and sparse online learning methods.

In the pursuit of justice and accountability in the digital age, the integration of Large Language Models (LLMs) with digital forensics holds immense promise. This half-day tutorial provides a comprehensive exploration of the transformative potential of LLMs in automating digital investigations and uncovering hidden insights. Through a combination of real-world case studies, interactive exercises, and hands-on labs, participants will gain a deep understanding of how to harness LLMs for evidence analysis, entity identification, and knowledge graph reconstruction. By fostering a collaborative learning environment, this tutorial aims to empower professionals, researchers, and students with the skills and knowledge needed to drive innovation in digital forensics. As LLMs continue to revolutionize the field, this tutorial will have far-reaching implications for enhancing justice outcomes, promoting accountability, and shaping the future of digital investigations.

Collocation of measurements from active and passive satellite sensors refers to the combination of data from two sensors that observe the same geographic area at nearly the same time but with differing spatial resolutions and viewing angles. This collocated data, often known as on-track data, comes with precise product labels from the active sensor but comprises only the pixels located directly on the path of an active satellite’s orbit. As a result, its spatial coverage is quite limited, especially when compared to the vast quantities of off-track data. Handling the abundant and information-dense off-track data is crucial for training machine learning models that can effectively integrate the unique features of this data along with on-track data. However, the sheer volume of off-track data presents significant challenges for these models. To address the challenges of large amounts of unlabeled off-track data in remote sensing applications, we introduce a self-supervised representation learning model with VAE and domain adaptation methods to learn a domain invariant classifier for the on-track and off-track data. The model’s performance is enhanced by pre-training off-track data with VAE generative model using off-track data, to learn a good representation that can be transferred to the down-streaming domain adaptation and classification tasks. The classifier is built on these representations to classify different cloud types in passive sensing data, with the goal of achieving higher accuracy in cloud property retrieval. Extensive quantitative and qualitative evaluation demonstrate our method achieves higher accuracy in cloud property retrieval for off-track remote sensing data.

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.

In the last decade or so, fairness in AI has received widespread attention, both within the scientific community and the general media. Researchers have made significant progress towards fairer AI, with work exploring everything from statistical definitions of fairness for individual and group fairness to fairness constraints and algorithms for debiasing models and datasets. Given the nascent nature of the field, however, progress in the space has been haphazard. For work in fair-AI to have as much real-world impact as possible, we need to take a step back and gauge the gaps and which research questions need urgent attention. This work analyzes where the field is currently and proposes more focused questions and new research areas within fair AI.

There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we study a longitudinal censored learning problem subject to fairness constraints, where we require that algorithmic decisions made do not affect certain individuals or social groups negatively in the presence of uncertainty on class label due to censorship. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world discriminated datasets with censorship demonstrate the practicality of our approach.

In this paper, we design and implement a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms. In this system, some important aspects like knowledge bases system, knowledge representation, inference engine have been addressed. The system asks users different questions and inference engines will use the certainty factor to prune out low possible solutions. The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system. Our expert system is generic and flexible, which can be integrated with any rule bases system in disease diagnosis.

Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of proteins (i.e., graph of amino acids). Existing work typically focuses on static rather than dynamic graphs, which are actually very important in the applications such as protein folding, molecule reactions, and human mobility. Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns. Here, this paper proposes a novel framework of factorized deep generative models to achieve interpretable dynamic graph generation. Various generative models are proposed to characterize conditional independence among node, edge, static, and dynamic factors. Then, variational optimization strategies as well as dynamic graph decoders are proposed based on newly designed factorized variational autoencoders and recurrent graph deconvolutions. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed models.

With the advent of data mining and business processes automation, outlier detection has evolved into a major problem attracting significant research in relation to several application domains. Further advances in Global Positioning system, tracking of anomalous events based on data enhances effective decision making and pro-active measures to overcome risks and avoid unwarranted outputs. Significant work has been done in trajectory outlier detection although no singular approach fits all the domains. By including position and collective outliers on the same visualizations will enhance understanding of an outlier behavior. As such, we have leveraged Hidden Markov Method for prediction-based point outlier detection and pattern mining to identify points or segments of outliers in trajectory data.

An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as well as the number of accidents and relevant damages are minimized. However, occupational-related death and injury are still increasing and have been highly attended in the last decades due to the lack of comprehensive safety management. A smart safety management system is therefore urgently needed, in which the staffs are instructed to fulfill responsibilities as well as automating risk evaluations and alerting staffs and departments when needed. In this paper, a smart system for safety management in the workplace based on responsibility big data analysis and the internet of things (IoT) are proposed. The real world implementation and assessment demonstrate that the proposed systems have superior accountability performance and improve the responsibility fulfillment through real-time supervision and self-reminder.

Predicting workplace injury using automated techniques opens newer possibilities in evidence-based research. This paper presents our preliminary research in a PhD project in predicting workplace incidents using machine learning algorithms. The analysis on the model performance using several mainstream machine learning algorithms including random forest, k-nearest neighbor and decision tree indicated that the general performance of the decision tree model was found to be statistically higher than that of the other two algorithms.

Effectively detecting the fraudulent invitations is valuable for many online Internet enterprises such as iQIYI to promote good products and improve the user experience. However, it remains highly non-trivial to address, which mainly lies in two challenging data characteristics. First, the invitation graph structure is globally large yet locally small, as a large number of invitations usually occur in a very small local graph, making the global and local consistency difficult to achieve simultaneously. Secondly, the user associations are heterogeneous and diverse, as the user associations are from multiple different data resources, making the effects of multiple user associations difficult to use effectively. To this end, this paper proposes a novel heterogeneous graph neural network HmGnn, to detect fraudulent invitations at iQIYI platform. To the best of our knowledge, this is the first attempt to study fraud invitation detection via graph neural networks. HmGnn handles the homogeneity and heterogeneity of networks simultaneously. Specifically, the proposal constructively introduces links between homogenous mini-graphs based on the similarity of mini-graphs, facilitating the impact of local mini-graphs to the global graph structure. In addition, this paper presents a heterogeneous attention convolution network to accurately optimize the contribution of multiple heterogeneous user associations. Extensive experiments conducted on real-world business data validate the excellent effectiveness and improvement on risk management of our method.

Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations.

This work stems from the Italian project H-CIM (Health-Care Intelligent Monitoring), aimed at developing a wearable sensor data streams based home-monitoring system to support self-rehabilitation of elderly outpatients. Different from the pervasive data stream applications, which are always accompanied by the evolution of unstable class concepts, this project requires stable standard and personalized rehabilitation exercises patterns be provided to assess outpatient's self-therapy progress at home. In this designed pipeline, the representation sequences of the personal standard rehabilitation exercises in wearable sensor streams is therefore first benchmarked, then an assessment system which integrates multistage data processing and analyzing is proposed to enable elders to manage their own rehabilitation progress properly. The system proved to be an effective tool for supporting compliance monitoring and personalized self-rehabilitation; it is currently under further development within the Italian project Home-IoT, with the aim to become a more general data stream analytics service, not only devoted to rehabilitation exercises assessment.

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Human posture recognition has a wide range of applications including elderly care and video surveillance. This paper discusses how to recognize human postures using wearable devices. From real-world data, we analyze the challenges in terms of result performance, recognition efficiency and sensor selection. To deal with the challenges, we present our design with five techniques: i) oversampling and undersampling methods, ii) ensemble learning, iii) sensor selection, iv) stream data classification and v) post-processing techniques. We verify our design and show our findings through extensive experiments on real-world data, which shows our approach can achieve up to 91.5% overall weighted average accuracy for all three postures. We also discuss possible extensions of our work.