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Social Aspects · Privacy-preserving Statistics and Machine Learning

Aviraj Newatia, Michael Cooper, Viet Nguyen, Rahul G. Krishnan

Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with a number of potential risks which increase over time, such as database breaches and malicious adversaries. Machine unlearning is the study of methods to efficiently remove the influence of training data subsets from previously-trained models. Existing unlearning methods typically require direct access to the "forget set"---the data to be forgotten-and organisations must retain this data for unlearning rather than deleting it immediately upon request, increasing risks associated with the forget set. We introduce partially-blind unlearning---utilizing auxiliary information to unlearn without explicit access to the forget set. We also propose a practical framework Reload, a partially-blind method based on gradient optimization and structured weight sparsification to operationalize partially-blind unlearning. We show that Reload efficiently unlearns, approximating models retrained from scratch, and outperforms several forget set-dependent approaches. On language models, Reload unlearns entities using <0.025\% of the retain set and <7\% of model weights in <8 minutes on Llama2-7B. In the corrective case, Reload achieves unlearning even when only 10\% of corrupted data is identified.

Computer Vision · 3D Rendering & Reconstruction

Lucas Maximilian Martini, Alex Lappe, Anna Bognár, Rufin Vogels, Martin Giese

The recognition of dynamic and social behavior in animals is fundamental for advancing several areas of the life sciences, including ethology, ecology, medicine and neuroscience. Recent progress in deep learning has enabled an automated recognition of such behavior from video data. However, an accurate reconstruction of the three-dimensional (3D) pose and shape has not been integrated into this process. Especially for non-human primates, the animals phylogenetically closest to humans, mesh-based tracking efforts lag behind those for other species, leaving pose descriptions restricted to sparse keypoints that are unable to fully capture the richness of action dynamics. To address this gap, we introduce the $\textbf{Big Ma}$ca$\textbf{Q}$ue 3D Motion and Animation Dataset ($\texttt{BigMaQ}$), a large-scale dataset comprising more than 750 scenes of interacting rhesus macaques with detailed 3D pose descriptions of skeletal joint rotations. Recordings were obtained from 16 calibrated cameras and paired with action labels derived from a curated ethogram. Extending previous surface-based animal tracking methods, we construct subject-specific textured avatars by adapting a high-quality macaque template mesh to individual monkeys. This allows us to provide pose descriptions that are more accurate than previous state-of-the-art surface-based animal tracking methods. From the original dataset, we derive BigMaQ500, an action recognition benchmark that links surface-based pose vectors to single frames across multiple individual monkeys. By pairing features extracted from established image and video encoders with and without our pose descriptors, we demonstrate substantial improvements in mean average precision (mAP) when pose information is included. With these contributions, $\texttt{BigMaQ}$ establishes the first dataset that both integrates dynamic 3D pose-shape representations into the learning task of animal action recognition and provides a rich resource to advance the study of visual appearance, posture, and social interaction in non-human primates. The code and data are publicly available at [https://martinivis.github.io/BigMaQ/](https://martinivis.github.io/BigMaQ/).

Social Aspects · Everything Else

Erfan Jahanparast, Zhiqing Hong, Serina Chang

What large language models (LLMs) know about human opinions has important implications for aligning LLMs with human values, simulating humans with LLMs, and understanding what LLMs learn during training. While prior works have tested LLMs' knowledge of opinions via their next-token outputs, we present the first study to probe LLMs' internal knowledge of opinions, evaluating LLMs across 22 demographic groups on a wide range of topics. First, we show that LLMs' internal knowledge of opinions far exceeds what is revealed by their outputs, with a 52-66\% improvement in alignment with the human answer distribution; this improvement is competitive with fine-tuning but nearly 300$\times$ less computationally expensive. Second, we find that knowledge of opinions emerges rapidly in the middle layers of the LLM and identify the final unembeddings as the source of the discrepancy between internal knowledge and outputs. Third, using sparse autoencoders, we trace the knowledge of opinions in the LLM's residual stream back to attention heads, and we identify specific attention head features that selectively encode different demographic groups. Through steerability experiments, we show that manipulating these features causally alters the LLM's outputs, aligning them more or less closely with different groups. These findings open new avenues for building value-aligned and computationally efficient LLMs, with applications in survey research, social simulation, and human-centered AI. Our code is available at https://github.com/schang-lab/llm-opinions.

Social Aspects · Everything Else

Lotem Peled-Cohen, Nitay Calderon, Roi Reichart

Can NLP predict heroin-addiction outcomes, uncover suicide risk, or simulate (and even influence) brain activity? Could LLMs one day contribute to research worthy of a Nobel Prize for advancing our understanding of human behavior? And what role do NLP scientists play in shaping that possibility? This post explores these questions, arguing that language technologies are not just tools that support scientific work (like literature search agents, writing tools, or coding assistants), but that by treating language as a window into the human mind, NLP and LLMs can actively help researchers uncover mechanisms of human behavior, cognition, and brain function.

Reinforcement Learning · Online

Seokbin Song, Youngsik Yoon, Siwei Wang, Wei Chen, Jungseul Ok

Combinatorial online learning is a fundamental task for selecting the optimal action (or super arm) as a combination of base arms in sequential interactions with systems providing stochastic rewards. It is applicable to diverse domains such as robotics, social advertising, network routing, and recommendation systems. In many real-world scenarios, we often encounter rising rewards, where playing a base arm not only provides an instantaneous reward but also contributes to the enhancement of future rewards, e.g., robots improving through practice and social influence strengthening in the history of successful recommendations. Crucially, these enhancements may propagate to multiple super arms that share the same base arms, introducing dependencies beyond the scope of existing bandit models. To address this gap, we introduce the Combinatorial Rising Bandit (CRB) framework and propose a provably efficient and empirically effective algorithm, Combinatorial Rising Upper Confidence Bound (CRUCB). We empirically demonstrate the effectiveness of CRUCB in realistic deep reinforcement learning environments and synthetic settings, while our theoretical analysis establishes tight regret bounds. Together, they underscore the practical impact and theoretical rigor of our approach.

Social Aspects · Fairness, Equity, Justice and Safety

Eunseo Choi, Ho-Yeon Kim, Jaewon Lee, Taeyong jo, Myungjun lee, Heejin Ahn

Quantifying uncertainty in neural network predictions is essential for deploying models in high-stakes domains such as autonomous driving, healthcare, and manufacturing. While conventional approaches often depend on costly sampling or parametric distributional assumptions, we propose Hyperspherical Confidence Mapping (HCM), a simple yet principled framework for uncertainty estimation that is both sampling-free and distribution-free. HCM decomposes model outputs into a magnitude and a normalized direction vector constrained to lie on a unit hypersphere, enabling a novel interpretation of uncertainty as the degree of violation of a geometric constraint. Grounded in this geometric constraint formulation, our method provides deterministic and interpretable uncertainty estimates applicable to both regression and classification. We validate the effectiveness of HCM across diverse benchmarks and real-world industrial tasks, demonstrating competitive or superior performance to ensemble and evidential approaches, while significantly reducing inference cost and ensuring strong confidence–error alignment. Our results highlight the value of geometric structure in uncertainty estimation and position HCM as a versatile alternative to conventional techniques.

Social Aspects · Fairness, Equity, Justice and Safety

Kyle O&amp;#x27;Brien, Stephen Casper, Quentin Anthony, Tomek Korbak, Robert Kirk, Xander Davies, Ishan Mishra, Geoffrey Irving, Yarin Gal, Stella R Biderman

Open-weight AI systems offer unique benefits, including enhanced transparency, open research, and decentralized access. However, they are vulnerable to tampering attacks which can efficiently elicit harmful behaviors by modifying weights or activations. Currently, there is not yet a robust science of open-weight model risk management. Existing safety fine-tuning methods and other post-training techniques have struggled to make LLMs resistant to more than a few dozen steps of adversarial fine-tuning. In this paper, we investigate whether filtering text about dual-use topics from training data can prevent unwanted capabilities and serve as a more tamper-resistant safeguard. We introduce a multi-stage pipeline for scalable data filtering and show that it offers a tractable and effective method for minimizing biothreat proxy knowledge in LLMs. We pretrain multiple 6.9B-parameter models from scratch and find that they exhibit substantial resistance to adversarial fine-tuning attacks on up to 10,000 steps and 300M tokens of biothreat-related text — outperforming existing post-training baselines by over an order of magnitude -- with no observed degradation to unrelated capabilities. However, while filtered models lack internalized dangerous knowledge, we find that they can still leverage such information when it is provided in context (e.g., via search tool augmentation), demonstrating a need for a defense-in-depth approach. Overall, these findings help to establish pretraining data curation as a promising layer of defense for open-weight AI systems

Social Aspects · Fairness, Equity, Justice and Safety

Yanping Li, Zhening Liu, Zijian Li, Zehong Lin, Jun Zhang

Fine-tuning large language models (LLMs) on custom datasets has become a standard approach for adapting these models to specific domains and applications. However, recent studies have shown that such fine-tuning can lead to significant degradation in the model's safety. Existing defense methods operate at the sample level and often suffer from an unsatisfactory trade-off between safety and utility. To address this limitation, we perform a systematic token-level diagnosis of safety degradation during fine-tuning. Based on this, we propose token-level data selection for safe LLM fine-tuning (TOSS), a novel framework that quantifies the safety risk of each token by measuring the loss difference between a safety-degraded model and a utility-oriented model. This token-level granularity enables accurate identification and removal of unsafe tokens, thereby preserving valuable task-specific information. In addition, we introduce a progressive refinement strategy, TOSS-Pro, which iteratively enhances the safety-degraded model's ability to identify unsafe tokens. Extensive experiments demonstrate that our approach robustly safeguards LLMs during fine-tuning while achieving superior downstream task performance, significantly outperforming existing sample-level defense methods. Our code is available at https://github.com/Polly-LYP/TOSS.

Sherzod Hakimov, David Semedo, Eric Müller-Budack, Marc A. Kastner 0001, Takahiro Komamizu

Multimodal human understanding is an evolving interdisciplinary field integrating computer science, psychology, and social sciences to model human perception, behaviour, and biases in multimodal data. While recent advancements in multimodal learning excel in tasks like image-text synthesis, they often overlook nuanced human-centric dynamics---such as cultural, political, and individual influences on how modalities (e.g., text and images) interact, complement, or contradict each other. The 4th International Workshop on Multimodal Human Understanding (MUWS) aims at addressing these challenges, fostering novel solutions that explicitly model human perception, behaviour, and biases in multimodal data, with a particular emphasis on real-world challenges in web and social media analysis. This year edition covers two tracks: (1) human-centred multimodal understanding, such as quantifying social biases, analysing sentiment and hate speech, and modelling cross-modal interactions through interdisciplinary theories (e.g., semiotics, gestalt psychology); and (2) Multimodal understanding of global events, supported by a newly curated dataset covering news articles with diverse stances, which facilitates research on cultural framing, societal impact, and bias mitigation in vision-language models. The event features two keynotes from renowned experts from journalism and computer science, research presentations for six accepted papers, and interactive discussions to explore and discuss cutting-edge methodologies and applications in multimodal human understanding. The workshop proceedings can be found at: https://dl.acm.org/doi/proceedings/10.1145/3728481

Tianjiao Xu, Hao Fu 0004, Suiyang Zhang, Jianhua Yin 0001, Tian Gan 0002, Liqiang Nie

Democratic mediation serves as a vital mechanism for resolving social conflicts; however, current practices encounter three critical limitations: (1) inefficient operations, wherein traditional laborintensive mediation processes are both time-consuming and inefficient; (2) theoretical gaps, as prevailing mediation theories fail to explore the underlying causes of conflicts; and (3) inadequate analysis, with existing digital tools lacking comprehensive conflict mediation capabilities and primarily focusing on singular data types. To address these limitations, we introduce the Normative Social Simulator for Democratic Mediation, referred to as Norm Mediat. This framework is specifically designed to simulate democratic mediation, incorporating social norms. Central to this framework is the integration of normative reasoning into the mediation process, which enhances the ability to understand individuals' intrinsic needs and identify the root causes of conflicts. The framework comprises two essential components: (1) Dynamic Multimodal Conflict Modeling (DMCM), which generates the initial dataset of conflict interactions; and (2) Norm-Aware Iterative Mediation (NAIM), which implements an iterative democratic mediation process through norm awareness. The results of our human evaluation underscore the effectiveness of our norm-driven mediation strategies. This research significantly contributes to computational social science by providing a comprehensive methodological framework for simulating democratic processes and offering a benchmark dataset for conflict resolution studies.

Jinghui Zhang 0002, Kaiyang Wan, Longwei Xu, Ao Li, Zongfang Liu, Xiuying Chen

Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for crisis management, policy-making, and social media analysis. However, existing works face notable limitations. First, they lack micro-level personalization, producing generic responses that ignore individual user preferences. Moreover, they overlook macro-level sentiment distribution and only deal with individual-level sentiment, constraining them from analyzing broader societal trends and group sentiment dynamics. To address these challenges, we propose SocialAlign, a unified framework that predicts real-world responses at both micro and macro levels in social contexts. At the micro level, SocialAlign employs SocialLLM with an articulate Personalized Analyze-Compose LoRA (PAC-LoRA) structure, which deploys specialized expert modules for content analysis and response generation across diverse topics and user profiles, enabling the generation of personalized comments with corresponding sentiments. At the macro level, it models group sentiment distributions and aligns predictions with real-world sentiment trends derived from social media data. To evaluate SocialAlign in real-world scenarios, we introduce SentiWeibo, a large-scale dataset curated from authentic social interactions on the Weibo platform. Experimental results on our SentiWeibo and related LaMP benchmark demonstrate that SocialAlign surpasses strong baselines, showing improved accuracy, interpretability, and generalization in public response prediction. We hope our work inspires further research in public response prediction and computational social science: https://github.com/Znull-1220/SocialAlign.

Govind Mittal, Sarthak Gupta, Shruti Wagle, Chirag Chopra, Anthony J. DeMattee, Nasir D. Memon, Mustaque Ahamad, Chinmay Hegde

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Election. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at https://wave-pulse.io

Chen Zhao 0010, Feng Chen 0001, Xintao Wu, Haifeng Chen

As computers increasingly make decisions about who gets a loan, a job, or even bail, the expansion of AI algorithms has provoked public concern about ethical issues, and the need to understand what constitutes AI algorithms and how they make decisions becomes ever more pressing. For example, an increasing number of high-profile news reports that widely-used algorithms have unfairly discriminated against some groups of people (e.g., by gender and race) in parole decisions and other major life events. Focusing more attention on ethical bias in learning algorithms is key to unlocking the potential of automated decision systems while ensuring fairness and accountability so that everyone can advance equally in society. Ethical AI has become increasingly important and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science.

Emre Eftelioglu, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Kathleen Buckingham, Auroop R. Ganguly, James Hodson 0003

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.

Aidong Zhang 0001, Vipin Kumar 0001, Yan Liu 0002

The past decade has been an inspiring time for artificial intelligence (AI) research. AI systems have transformed norms and practices across industries and have permeated the fabric of human society. Moreover, AI is ushering in a transformative technological age by making remarkable breakthroughs in a number of scientific fields such as protein structure prediction and medical imaging. There is increasing consensus in the wider scientific community that AI is poised to disrupt science by unlocking entirely new approaches, driving new scientific inquiry, and enabling greater scientific leaps with far-reaching social consequences. However, there are substantial barriers preventing science from realizing that potential, and addressing these barriers will require support for advances in AI methods and the adoption of these methods in routine scientific research. In this special day at KDD 2025, we host a series of talks by distinguished researchers on AI for science.

Ebrahim Bagheri, Faezeh Ensan, Calvin Hillis, Reihaneh Rabbany, Robin Cohen, Benjamin C. M. Fung, Sébastien Gambs

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore how data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

Yule Liu, Zhiyuan Zhong, Yifan Liao, Zhen Sun 0001, Jingyi Zheng, Jiaheng Wei, Qingyuan Gong, Fenghua Tong, Yang Chen 0001, Yang Zhang 0016 等

The rising popularity of large language models (LLMs) has raised concerns about potential abuse and harmful content. As a result, developing a highly generalizable and adaptable machine-generated text (MGT) detection system has become an urgent priority. Given that LLMs are most commonly misused in academic writing, this work investigates the generalization and adaptation capabilities of MGT detectors in three key aspects specific to academic writing: First, we construct MGT-Academic, a large-scale dataset comprising over 336M tokens and 749K samples. MGT-Academic focuses on academic writing, featuring human-written texts (HWTs) and MGTs across STEM, Humanities, and Social Sciences, paired with an extensible code framework for efficient benchmarking. Second, we benchmark the performance of various detectors for binary classification and text attribution tasks in both in-domain and cross-domain settings. This benchmark reveals the often-overlooked challenges of text attribution tasks. Third, we introduce a novel text attribution task in which models must adapt to new classes over time, with little or no access to prior training data, spanning both few-shot and many-shot scenarios. We implement a range of adaptation techniques to enhance performance across these settings. Our findings provide new insights into the generalization ability of MGT detectors and lay the foundation for building robust, adaptive detection systems. The code framework is available at https://github.com/Y-L-LIU/MGTBench-2.0.

Junhong Lin 0004, Xiaojie Guo 0002, Shuaicheng Zhang, Yada Zhu, Julian Shun

Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and heterophily, where connected nodes may have dissimilar labels and attributes. However, existing benchmarks primarily focus on either heterophilic homogeneous graphs or homophilic heterogeneous graphs, leaving a significant gap in understanding how models perform on graphs with both heterogeneity and heterophily. To bridge this gap, we introduce H2GB, a large-scale node-classification graph benchmark that brings together the complexities of both the heterophily and heterogeneity properties of real-world graphs. H2GB encompasses 9 real-world datasets spanning 5 diverse domains, 28 baseline models, and a unified benchmarking library with a standardized data loader, evaluator, unified modeling framework, and an extensible framework for reproducibility. We establish a standardized workflow supporting both model selection and development, enabling researchers to easily benchmark graph learning methods. Extensive experiments across 28 baselines reveal that current methods struggle with heterophilic and heterogeneous graphs, underscoring the need for improved approaches. Finally, we present a new variant of the model, H2G-former, developed following our standardized workflow, that excels at this challenging benchmark. Both the benchmark and the framework are publicly available at Github and PyPI, with documentation hosted at https://junhongmit.github.io/H2GB.

Erica Coppolillo, Federico Cinus, Marco Minici, Francesco Bonchi, Giuseppe Manco 0001

Large Language Models (LLMs) demonstrate significant persuasive capabilities in one-on-one interactions, but their influence within social networks, where interconnected users and complex opinion dynamics pose unique challenges, remains underexplored. This paper addresses the research question: Can LLMs generate meaningful content that maximizes user engagement on social networks? To answer this, we propose a pipeline using reinforcement learning with simulated feedback, where the network's response to LLM-generated content (i.e., the reward) is simulated through a formal engagement model. This approach bypasses the temporal cost and complexity of live experiments, enabling an efficient feedback loop between the LLM and the network under study. It also allows to control over endogenous factors such as the LLM's position within the social network and the distribution of opinions on a given topic. Our approach is adaptive to the opinion distribution of the underlying network and agnostic to the specifics of the engagement model, which is embedded as a plug-and-play component. Such flexibility makes it suitable for more complex engagement tasks and interventions in computational social science. Using our framework, we analyze the performance of LLMs in generating social engagement under different conditions, showcasing their full potential in this task. The experimental code is publicly available at https://github.com/mminici/Engagement-Driven-Content-Generation.

Hao Wu 0094, Haomin Wen, Guibin Zhang, Yutong Xia, Yuxuan Liang 0002, Yu Zheng 0004, Qingsong Wen, Kun Wang 0056

The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting obstacle to their industrial level deployment. Concretely, earth science systems rely heavily on the extensive deployment of sensors, however, the data collection from sensors is constrained by complex geographical and social factors, making it challenging to achieve comprehensive coverage and uniform deployment. To alleviate the obstacle, traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors. These methods dynamically adjust the activation times of sensors to optimize the detection process across each sub-region. Regrettably, formulating an activation strategy generally based on historical observations and geographic characteristics, which make the methods and resultant models were neither simple nor practical. Worse still, the complex technical design may ultimately lead to a model with weak generalizability. In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions. To our knowledge, this is the first proposal (termed DynST) of an industry-level deployment optimization concept at the data level. However, due to the existence of the temporal dimension, pruning of spatio-temporal data may lead to conflicts at different timestamps. To achieve this goal, we employ dynamic merge technology, along with ingenious dimensional mapping to mitigate potential impacts caused by the temporal aspect. During the training process, DynST utilize iterative pruning and sparse training, repeatedly identifying and dynamically removing sensor perception areas that contribute the least to future predictions. DynST demonstrates tremendous capability on industrial-grade data from JD Technology TaxiBJ+ and practical deployment scenarios such as meteorology, combustion dynamics, and turbulence. It seamlessly integrates with relevant models and efficiently prunes image and graph-type data, leading to significantly higher inference speeds without introducing noticeable performance degradation.