Large language models (LLMs) are increasingly used for learning, raising a key question: can learners remain epistemically proactive, retaining responsibility for the cognitive and regulatory activities through which learning is produced while working with AI? Because this responsibility may change as interactions unfold, we examine epistemic proactivity as a developing process. We conceptualize epistemic proactivity through self-regulated learning (SRL) moves, complemented by help-seeking strategies and manifestations of epistemic vigilance. We analyzed these behaviors in 880 substantive turns from 112 secondary-school students completing open-ended mathematical-modeling practice with a general-purpose LLM (Mistral). Overall, students showed limited observable epistemic proactivity: their interactions were strongly request-dominated, with planning, monitoring, and evaluation relatively rare, although low-quality AI responses elicited more explicit evaluation. Temporal analyses qualified this picture: explicit SRL-related moves increased modestly over the chat, but help-seeking strategies became less understanding-oriented. Crucially, these temporal trajectories in help-seeking strategies predicted their post-test performance without AI beyond pre-test and aggregate patterns: students whose requests became more understanding-oriented performed better once AI assistance was removed. Thus, while students had a limited epistemic proactivity overall, its temporal development still was a critical predictor of post-test performance without AI. These results underscore the importance of examining the temporal organization of epistemic proactivity, rather than isolated prompt types, for understanding AI-supported learning. We release our fully-anonymized dataset publicly in an OSF repository.
Evangelos Apostolakis, Erfan Hamdi, Sara Jahedazad, Emma Lejeune
Large language models (LLMs) have rapidly become a default source of help for students in science and engineering courses. In foundational engineering mechanics courses such as statics, students routinely use these tools to check answers, work through partial solutions, and ask for explanations. Effective tutoring in this setting requires more than a correct final answer. A model must also confirm correct work when a student doubts it, and identify errors in flawed work. Existing benchmarks evaluate LLMs primarily on final-answer accuracy, so it is unclear whether models that can solve mechanics problems also have the other technical capabilities that tutoring depends on. Here we introduce TrussTutor-Bench, a benchmark of 200 verified two-dimensional truss problems paired with automatically generated student solutions, and use it to evaluate thirteen LLMs on solution accuracy, sycophancy, and error detection. Baseline accuracy ranged from 64.5% to 97.0%. Every model detected nearly all injected errors in synthetic flawed student work (90.4% to 100%), so error detection did not distinguish models under these clean conditions. Sycophancy did. When a student insisted that correct work was wrong, models rejected correct solutions to problems they had themselves solved in 1.6% to 50.0% of cases. These results show that performance varies widely across models: the leading models solve, check, and defend student work nearly perfectly, while others are far less reliable, particularly when a student doubts correct work. TrussTutor-Bench provides a verified, extensible standard for determining which models are technically reliable enough to put in front of students. Follow-up work must now focus on teaching rather than technical capability, drawing on learning science to determine whether LLM help leads students to understand the material or simply to reach the right answer.
Yin Fang, Qiao Jin, Shubo Tian, Lauren He, Maya Geer, Noor Naffakh, Ryan Huu-Tuan Nguyen, Zifeng Wang 等 27 位作者
Clinical trials are essential for evidence-based medicine and drug development, but many fail because of insufficient patient enrollment. Although there is growing interest in using AI to support patient recruitment, existing systems largely perform eligibility assessment alone and have rarely been evaluated in real-world workflows. Here we present TrialGPT 2.0, an AI-assisted clinical trial recommendation system improved for responsible real-world deployment. Rather than asking only whether a patient may qualify, the system assesses which trials warrant further consideration given the patient's current clinical needs and local workflow priorities, and provides structured, inspectable explanations for expert review. Importantly, we evaluated TrialGPT 2.0 retrospectively and prospectively across government research, patient-advocacy, academic medical center, and community-based radiation-oncology settings. In retrospective multicenter cohorts comprising 288 cases, TrialGPT 2.0 retrieved at least one clinician-recommended trial in its top 10 recommendations for approximately 91% of cases while reducing clinician screening time by 55.0%. In a six-month prospective evaluation embedded in an active precision oncology tumor board, TrialGPT 2.0 contributed additional trial opportunities missed by the routine workflow, increasing the number of patient cases with at least one final trial recommendation by 90.9%. To support scientific reproducibility, we also developed NIH-TrialBench, a clinician-authored benchmark comprising 126 diverse synthetic patient vignettes and matching scenarios from 11 NIH Institutes and Centers. Together, these findings support the value of AI to assist clinical trial matching by improving clinician efficiency and identifying frequently overlooked trial opportunities, ultimately helping to expand and accelerate accrual to cancer trials.
Accessibility assessment underpins the right to participate in heritage tourism, yet evaluation of archaeological sites remains artisanal: fieldwork does not scale, is often carried out without accessibility training, and is reported in forms that reach neither the sector nor disabled visitors. AI appears to offer a shortcut. We argue that assessment is measurement and inference under sparse, expert-mediated evidence: AI transforms how that evidence is captured and communicated, whereas evaluation, which turns evidence into defensible judgements, demands properties current generative systems lack. In a five-park field study in Spain and Italy, an AI desk audit scored only 66% of dimension-site combinations, could not observe embodied and organisational dimensions, and mirrored sites' online self-presentation. Simple Bayesian models instead quantify the confidence rankings deserve and calibrate desk evidence against fieldwork. We propose a separation of roles between AI and interpretable statistical evaluation as the basis for a reliable accessibility atlas.
Michal Orzechowski, Lukasz Opiola, Ignacio Lamata Martinez, Marinos Ioannides, Panayiotis N. Panayiotou, Lukasz Dutka, Renata G. Slota, Jacek Kitowski
The complexity of high-quality 3D digitised cultural heritage objects creates challenges for existing data management systems as they need to develop metadata management and processing capabilities to provide semantic insight into the interconnectivity of data that constitutes cultural heritage objects. To address these challenges, we propose a global federated authentication and authorisation mechanism, a data and metadata management system, and an integrated engine for designing and executing automated workflows that facilitate the processing of both data and metadata. The solution is evaluated with three distinct 3D digitised cultural objects and presents the complete process from data upload to cultural heritage object publication
Samuel Wairimu, Leonardo Horn Iwaya, Ala Sarah Alaqra
Organisations frequently utilise Privacy Impact Assessments (PIAs), Data Protection Impact Assessments (DPIAs), or even Fundamental Rights Impact Assessments (FRIAs) to identify and evaluate potential risks and possible infringements of fundamental rights. While the adoption of PIAs and DPIAs has grown significantly in recent years, robust scientific evidence regarding their operational challenges, implementation strategies, and tangible benefits remains scarce. In particular, empirical research conducted post-enactment of the General Data Protection Regulation (GDPR) is notably limited -- especially studies that directly interview professionals about their PIA/DPIA practices or their engagement with FRIAs. To do so, in this paper we leveraged semi-structured interviews to elicit privacy professionals' perspectives on PIA/DPIA activities and analysed the responses using reflexive thematic analysis. As a result, this work provides a comprehensive thematic framework for the field, offering a rich and thick description of professionals' views and experiences. In total, 26 participants from 17 countries were interviewed, revealing how they plan and conduct PIAs/DPIAs, including how they assess trade-offs, the benefits and challenges they perceive, and how they view the interplay between PIAs/DPIAs and the EU AI Act. Among the main findings were low adherence to established DPIA methodologies; differences and similarities between PIAs and DPIAs; a perceived neglect of data subjects' interests during DPIA processes; discussions on building FRIAs on top of DPIAs; and the potential for holistic impact assessments, though accompanied by concerns regarding assessment fatigue. Thus, this study informs privacy professionals about real-world challenges and experiences of PIA/DPIA implementation, and guides researchers toward future research directions.
LLMs increasingly fact-check online content with the poster's identity in view. However, it remains unclear which post-training changes how much a model's truth judgement depends on the poster's social class, and how such a change happens inside the model. This paper investigates job-based doubt, i.e., judging a claim false more often when the poster has a lower-status job, using fact-checked claims with randomised poster profiles in base and post-trained checkpoints of six model families. Our results reveal that: 1) post-training creates such doubt only with the open Tülu-3 recipe (OLMo-2 32B, Tülu-3 70B), not with Meta's, Alibaba's or Mistral's; the effect is under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; 2) in OLMo-2 32B, the model does not start noticing the poster's job more, but starts interpreting it differently: a lower-status job pushes the base model toward "true" and the post-trained model toward "false"; and 3) this flip is carried by a single residual-stream direction and happens at supervised fine-tuning. The mechanism was confirmed on 400 new claims with pre-specified predictions. None of seven deployed commercial models doubts lower-status posters more. Our work underscores the need to audit speaker bias per training recipe.
We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem: existing generative methods rely on surface-level textual features and fail to capture latent user motivations, limiting personalization depth and recommendation interpretability. Our approach leverages Multi-LLM Interest Mining (MLIM) via structured reasoning prompting, Reward-Labeled Deep Interest (RLDI) for quality control, and Interest-Enhanced Item Discretization (IEID) via RQ-VAE, combined with a two-stage SFT-GRPO training pipeline guided by an Interest-Aware Reward. We validate DeepInterestGR on three Amazon Review benchmarks (Beauty, Sports, Instruments), comparing against 14 state-of-the-art baselines including SASRec, BERT4Rec, TIGER, LC-Rec, and S-DPO. Our method achieves 12.4%-15.4% relative improvements on HR@10 and 15.5%-17.9% on NDCG@10, with cross-domain generalization gains of +24.8%. To our knowledge, this is the first work to demonstrate such significant gains by incorporating deep semantic interests into generative recommendation.
Joseph Walusimbi, Ann Move Oguti, Joshua Benjamin Ssentongo, Keith Ainebyona
Artificial intelligence and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, personalised explanations, and inquiry-driven learning. However, most AI-based learning systems rely on continuous internet connectivity and cloud-based computation, limiting their use in bandwidth-constrained environments. This paper presents Arapai, an offline-first large language model architecture designed for AI-assisted learning in low-connectivity settings. The system performs all inference locally using quantized language models and incorporates hardware-aware model selection to enable deployment on low-specification, CPU-only devices. By removing dependence on cloud infrastructure, the system provides curriculum-aligned explanations and structured academic support through natural-language interaction. To support learners at different educational stages, the system includes adaptive response levels that generate explanations at varying levels of complexity: Simple English, Lower Secondary, Upper Secondary, and Technical. The system was evaluated with 120 students and 9 instructors from secondary and tertiary institutions under limited-connectivity conditions. Results indicate stable operation on legacy hardware, acceptable response times of 1-3 seconds for typical queries, and positive user perceptions of its effectiveness in supporting self-directed learning.
Large language models are increasingly used for financial guidance, yet little is known about whether their advice changes across social identities in Global South contexts. We introduce the Indian Financial Automated Advice Bias (IFAAB) benchmark, a counterfactual audit dataset of 31,734 GPT-4o-generated financial-advice responses for synthetic Indian personas varying by gender, caste, education, employment, residence, age, and district. Holding the underlying request fixed, we evaluate disparities in LLM-implied wealth, income, savings, projected gains, textual similarity, advice topics, and qualitative bias judgments. In the gender-only audit, male-labelled profiles receive 60.1\% higher initial total resources and 46.8\% higher post-advice total resources than comparable female-labelled profiles; in the caste-augmented audit, the post-advice total-resource gap remains 49.4\%. Adding district information reduces the aggregate post-advice total-resource gender gap to 7.9\%, revealing strong geographic conditioning. Standalone caste comparisons also show systematic stratification: relative to General-category profiles, OBC, SC, and ST profiles receive significantly lower post-advice total resources, respectively. Textual audits show low semantic similarity between paired gendered responses, with an average TF--IDF cosine similarity score of 0.44. Qualitative analysis shows that women are more often steered toward cautious saving, community support, and basic financial literacy, while men receive more growth-oriented advice involving investment, entrepreneurship, and asset accumulation. IFAAB provides a reusable benchmark for evaluating fairness, suitability, and consumer-protection risks in LLM-mediated financial advice, and highlights the need for locally grounded, intersectional evaluation of AI systems in high-stakes economic domains.
Tara Azin, Christy Caudill, Mako Sorensen, Peter Pulsifer
As artificial intelligence systems increasingly rely on vast textual data, respectful and accurate representation of Indigenous Knowledge has become critical. This paper introduces IKTRACE, a natural language processing framework for identifying, analyzing, and organizing Indigenous Knowledge representations in academic literature while adhering to Indigenous Data Sovereignty (IDsov) principles. We present a community-informed taxonomy co-developed with Inuit partners, comprising nine knowledge domains grounded in Indigenous Data Sovereignty principles. Building on this taxonomy, we implement a multi-method Knowledge identification pipeline using complementary natural language processing approaches. Outputs are integrated into a knowledge graph to support future expert and community validation. Applied to a corpus of Canadian Arctic research documents, IKTRACE demonstrated high accuracy and strong community relevance in expert validation. Our framework provides a scalable, ethically grounded foundation for Community-centered Knowledge validation and the development of Indigenous-informed AI systems.
Deepfake detection is typically evaluated as a media-classification task: given a clip, determine whether it is synthetic. Yet benchmark performance is increasingly invoked to support broader claims about preventing deepfake-enabled deception in video calls, voice calls, and targeted communications. We argue that these claims require evidence across two distinct validity gaps. External validity concerns whether detection performance transfers across generators, processing pipelines, and communication channels. Consequential validity, as used here, concerns whether reliable detection translates into timely intervention and prevention of harmful actions. Even perfect synthetic-origin classification does not guarantee prevention when verification is unavailable, intervention occurs too late, or recipients disregard warnings. We therefore propose a scenario-level evaluation framework that integrates media evidence, interaction-level indicators grounded in speech act theory, conversational pragmatics, and influence principles, and operational outcomes. We define candidate measures of attack prevention, benign pass-through, intervention precision under explicit deployment base rates, and intervention timing. Our position is that deployment-oriented evaluation must measure not only whether synthetic media is detected, but whether deception is prevented without unnecessarily disrupting legitimate interactions. https://jesseeho.github.io/deepfake-deception/
Generative AI now produces images and poems that people cannot reliably distinguish from human-made work, so people often judge for themselves who made a work. We examine how these perceived-authorship judgments relate to aesthetic preference. In a public online AI Art Turing Test, 11,123 people judged whether each of 50 pictures (26 AI-generated) was made by a human and were asked for their favourite. Among the 9,152 respondents who judged their favourite, a picture was 22.1 percentage points more likely to be judged human-made when it was the respondent's favourite, holding respondent and picture fixed. The association was similar for AI-generated and human-made favourites, was positive for all 50 pictures, and appeared under every reported detection strategy. Respondents most negative about AI art chose fewer AI-generated favourites (47% against 60% among the most positive) but more often believed them human-made, partly because others also judged those pictures human. As a result, in every opinion group a third to two fifths of respondents named an AI-generated favourite they believed human-made. A reanalysis of a published experiment on AI-generated poetry, in which 230 people rated each poem before judging its authorship, found the same pattern, although the AI poems were rated higher. When provenance is hidden, liking and perceived human authorship travel together, consistent with aesthetic response serving as evidence of a human maker and with beliefs about the maker shaping enjoyment. The findings qualify research on bias against AI art, which typically uses explicit labels, and inform AI disclosure policy.
As generative artificial intelligence (AI) settles into everyday practices of inquiry, an old epistemological question returns in a new guise: when does a proposition a user takes from an AI output become something the user knows, and when is it rational to trust it as such? This paper argues that the characteristic failures of AI-mediated inquiry are precisely those that the justified-true-belief (JTB) tradition and its post-Gettier anti-luck refinements were built to diagnose, and it advances three claims. First, AI outputs inherit neither the presumptive warrant of testimony nor the reliabilist warrant of deterministic tools: unaudited trust in a calculator rests on a type-level reliability record, and for general-purpose generative models there is no type-stable process to which such a record could attach -- a practical form of the generality problem. Second, use that stops at obtaining an output turns generative AI into a Gettier machine: the user's fluency-based seeming of justification is disconnected from the truth of what is believed, and, as a clock running ten times too fast illustrates, sheer speed of production multiplies coincidental hits, inflating the appearance of reliability without improving the standing of any single hit. Third, the remedy is a three-step structure of use -- acquisition, understanding, independent verification -- whose first two steps answer to the classical belief and justification conditions and whose third answers to the post-Gettier sensitivity condition. The result is a stakes-indexed criterion of rational trust, justified true output (JTO), with an everyday test: "Had this been false, would I have noticed?" The criterion is defended against objections from over-demandingness, the extended mind, model improvement, and redundancy.
This study presents AiAWE, an open-source automated writing evaluation system that scores argumentative essays with a LoRA-adapted large language model deployed on local hardware. Using 480 TOEFL Independent Writing essays from a restricted-access Educational Testing Service (ETS) dataset, we fine-tuned Gemma-3-27B and LLaMA-3.3-70B under identical LoRA configurations on 120 essays from one prompt, with all hyperparameters fixed in advance, and evaluated them on the remaining 360 essays. Gemma achieved a root mean square error (RMSE) of 0.474 and a quadratic weighted kappa (QWK) of 0.828 on the half-point grid against the ETS scores. In paired comparisons, it significantly outperformed a fine-tuned GPT-3.5 baseline from prior work on every metric tested, showing that a locally deployable open-weight model can achieve competitive scoring performance in this setting. It also had significantly lower error than the larger LLaMA configuration, although, because the two models differ in family as well as size, this comparison does not isolate the effect of scale. Neither open-weight model showed a statistically detectable increase in error on the 240 essays written to a second, unseen prompt from the same task family. On a single consumer-grade GPU, the scoring model returned a complete response in a median of 14.2 s, demonstrating technical feasibility under the tested conditions. Only the scoring component was evaluated, not the generated feedback. The system is publicly accessible at https://app.awade.gec.waseda.ac.jp, and its code, LoRA adapters, and training configurations are openly released.
Tanvir Hossain, Mahmudul Hasan, Kareem El-Araby, Lorenzo R. Godinez, Swarup Bhunia, Md Tauhidur Rahman, Tamzidul Hoque
Cybersecurity education has predominantly focused on software and network security, under the assumption that the underlying hardware is trustworthy. Threats introduced through the global semiconductor supply chain, together with vulnerabilities arising during design and integration, challenge the assumption that hardware can serve as an unquestioned root of trust. Sensor-enabled systems and machine-learning accelerators further expand the hardware attack surface. The undergraduate curriculum of electrical and computer engineering and computer science programs has not evolved accordingly. Core courses cover the design and the functionality of hardware while the security aspects remain overlooked, and dedicated hardware security (HS) courses are elective, scarce, and often restricted to graduate students. At the University of Florida, fewer than 7% of the undergraduate electrical and computer engineering students register for the introductory HS course each year. We present an integrated and economical approach that disseminates fundamental HS concepts within existing core courses through contextual learning. Nine hands-on modules were developed such that each security concept exercises an associated design concept of the core course, as a component of that course and not as a course of their own. Doing the security work is how the student exercises the design topic, so the modules are built to strengthen the host course and not only to add to it. Three were assessed across three offerings of the embedded systems course through one course survey and two conducted by an external evaluator, with 25 respondents. We report what those instruments measured and the limitations of the approach.
This paper develops a critical theoretical framework for understanding national Artificial Intelligence (AI) strategies through the lens of "Governance as Infrastructure." Integrating insights from Infrastructure Studies, Science and Technology Studies (STS), this paper employs Black Feminist Technoscience (BFTS) to reveal how AI governance infrastructures are assembled, maintained, and contested, and whose forms of knowledge, labour, and experience are rendered visible or invisible in these processes. We analyze the Canadian AI governance landscape through in-depth interviews with bureaucrats, civil society advocacy groups, academic researchers, and industry actors. Our inductive thematic analysis reveals how AI governance infrastructure enacts specific configurations of power, particularly through the politics of expertise; how actors navigate profound ambiguities related to AI's opacity, often involving strategic ignorance; and how mechanisms for justice and accountability struggle to address AI-related harms. We contend that a BFTS perspective highlights the limitations of current governance approaches and points toward alternative infrastructural designs prioritizing care, maintenance, participatory democracy, and adaptive temporalities to foster more equitable and accountable technology governance.
Marilou O. Espina, Sales G. Aribe, Mark Ian M. Mukara, Joseph L. Abella, Nurwahyu Alamsyah
IPOs contribute significantly to local economies, cultural preservation, and sustainable livelihoods within their ancestral communities. However, many IPOs remain marginalized in digital economies due to limited digital competencies, inadequate access to technology, and insufficient legal awareness related to online enterprise participation. This study examined the marketing practices, digital competencies, and legal-literacy needs of IPOs in the Philippines and developed a legal-digital empowerment framework for higher education extension and community engagement initiatives. Using a sequential mixed-methods design, data were collected through surveys and focus group discussions involving IPO members engaged in community-based enterprises. The study assessed existing marketing practices, identified competency and legal-awareness gaps, and determined capacity-building needs related to digital enterprise participation. Findings revealed that IPOs continue to rely heavily on traditional marketing channels, with limited use of digital platforms, inconsistent branding practices, and minimal engagement in strategic digital marketing activities. Participants also expressed concerns regarding intellectual property protection, online participation, and limited understanding of legal mechanisms related to digital enterprise engagement. Despite these limitations, IPO members showed strong willingness to participate in training and mentorship initiatives. The study proposes the Legal-Digital Empowerment Framework for Indigenous Community Enterprises, which integrates digital competencies, legal literacy, institutional support systems, and rights-based market participation into a structured and context-responsive model for higher education extension programs.
Recommendation-driven platforms can reshape not only what users see, but also who becomes visible and how community boundaries are negotiated. We examine Xiaohongshu discussions surrounding a perceived World Cup-related audience influx to understand how users attributed community change and how those explanations related to boundary-management strategies. We analyzed 262 comments from eight high-engagement threads using researcher-led thematic analysis, structured coding, and permutation-based association analysis. Users described personalization as both social separation and a boundary that could fail, interpreted platform growth through audience acquisition and commercialization, and contested whether gender should define community membership. Among 70 comments with explicit attributions and strategies, attribution and strategy were strongly associated (Cramer's V = .583): algorithmic attributions concentrated in technical boundary management, while social-actor attributions more often aligned with social enforcement or normative socialization. These findings connect causal sensemaking with boundary management and show how audience restructuring can create governance consequences on recommendation-driven platforms.
Most instruments for measuring political positions, such as manifesto coding, expert surveys and text-scaling models, were built and validated on Western party systems, and they break down worst where many consequential actors never publish a manifesto, never contest a competitive election, or commit one position to a charter and act on another in office. This paper proposes a method for such settings. I treat a large language model as a single fallible rater in a panel, the way an expert survey treats one expert, and describe the panel, its synthesis, an applicability rule that keeps a blank distinct from a zero, and a lens that separates what an actor declares from what it does. Three results follow. First, a built-in experiment isolates the rubric: with a definition-free round held fixed, written axis definitions move scores by a mean of 1.8 points on a 21-point scale and tighten agreement between raters (mean absolute gap 2.81 to 2.50; r 0.81 to 0.89). Second, across nine models from eight laboratories in two countries, Krippendorff's alpha is 0.86 on an interval and 0.87 on an ordinal metric, and it held as the panel grew from five raters to nine. This is reliability and says nothing yet about validity, since models trained on overlapping data can share a bias no statistic computed from the models can detect. Third, the panel's sharpest split, a bimodal divergence on an actor's stance toward the state's foundational order, comes from a referent problem: the models agree on the facts and differ on which order counts as the regime. A blind triple-coding traces about two-thirds of such disagreement to interpretation. The worked example is the Middle East and North Africa, and the rubric, prompt and data are released in full.
Large language models are increasingly used as proxies for human subjects in social science research, yet external validity requires matching the response distributions of target human populations. We study population-level survey alignment: reconstructing aggregate survey responses from limited public survey data, without individual-level demographic profiles or model finetuning. We formalize this problem as preference reconstruction: rather than matching proxy agents to demographic profiles, we construct a functional basis of proxy agents and recover population preferences by weighted aggregation. We instantiate this idea via Prompts to Proxies ($\texttt{P2P}$), a two-stage inference-time system. Stage 1 uses structured attribute-based prompting with entropy-guided adaptive sampling to construct a diverse proxy pool spanning the latent preference space. Stage 2 employs L1-regularized regression to select a compact weighted ensemble matching observed target-population responses. Across 14 American Trends Panel waves, $\texttt{P2P}$ achieves an average test MSE of 0.014 at approximately 0.8 USD per survey, improving over prompting and demographic-conditioning baselines. On the World Values Survey, cross-locale transfer experiments show that basis expressiveness can outperform locale matching on average, while locale-specific generation helps on culturally divergent questions. A stress test against an SFT-aligned survey model shows competitive performance using less than 3\% of the training data. These results position preference reconstruction as a lightweight, externally verifiable alternative for survey-based population alignment. Code is available at https://github.com/bingchen-wang/P2P.
Haiwen Li, Crystal Qian, Ben Usman, Stephanie Chan, Michiel Bakker, Srivignesh Rajendran, Avneesh Sud, Thomas Leung 等 12 位作者
As AI systems increasingly mediate human communication, identifying conditions under which they augment collective sense-making is a critical challenge. Through a randomized experiment (N=1,173 U.S. participants across 391 small-group deliberations), we investigate whether AI-guided self-reflection improves subsequent deliberative discourse. We show that engaging with a non-directive AI chatbot instructed to structure self-reflection prior to group deliberation yields significantly higher deliberation quality (measured by the Discourse Quality Index, DQI) compared to a baseline of no self-reflection (Cohen's d=0.31) and a condition of unassisted self-reflection (d=0.27), and the positive impact is most pronounced in groups with disagreement. This improvement may be driven by more sophisticated argument justifications, greater consensus-building effort, and increased respect for counterarguments. Furthermore, after self-reflection, participants in the AI condition reported higher empathy toward dissenting peers and a deeper understanding of opposing reasoning. Beyond observed behavior, our findings are empirically relevant to open debates between political philosophy and psychology surrounding self-reflection, demonstrating that interactive AI could transform solitary thinking into productive internal reflection. Together, these results demonstrate that even simple AI-based reflective scaffolds can foster more productive group dynamics, opening new avenues for leveraging AI to augment collective intelligence.
Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, José Adenaldo Santos Bittencourt, Felipe Fanchini
Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external references. Quantum classifiers may achieve competitive aggregate performance while still exhibiting subgroup disparities. Since a smaller subgroup AUC gap may stem from degrading the best group rather than improving the worst, each mitigation is also tested against a pre-specified non-inferiority margin relative to its baseline. Under this criterion, several configurations with the smallest disparity, notably QCNN with GRL on DDI, are not clinically acceptable due to loss of global discrimination. On DDI, QSVM with GRL+EO performed best and approached PanDerm in the performance/fairness trade-off; on CheXpert, QCNN with GRL achieved the highest aggregate performance but increased disparity, and EVA-X remained stronger. Fairness effects are model- and dataset-dependent, so QML requires empirical subgroup-aware evaluation. All quantum models run in classical simulation; we claim no quantum advantage.
Notice-and-comment rulemaking gives any affected party the same formal right to influence federal regulation, but formal access is not substantive capacity to shape rule text. Existing measurement strategies operate at the rule or aggregate-corpus level, too coarse to capture the discrete regulatory obligations where commenters seek change. We introduce obligation-level responsiveness auditing, an auditable, AI-assisted framework for measuring whether public-comment engagement co-occurs with changes to specific regulatory obligations. The framework extracts proposed and final-rule obligations, matches comments to the obligations they address, and classifies proposed-to-final outcomes; we test each of these AI-assisted components against blind human judgment. We apply the framework to 70,075 comments across 36 EPA anchor rulemakings, drawn from a corpus of 786,197 comments across 6,145 dockets from 2010-2022. Three descriptive findings emerge. First, engagement is associated with revision across rulemakings, but not clearly within the same rulemaking. Second, support-versus-opposition direction does not clearly differentiate outcomes, an informative null inconsistent with simple preference aggregation. Third, under a permissive reconstruction of commenter type, organizational-majority engagement concentrates in editorial-refinement rather than substantive-modification outcomes across rulemakings. A blind human audit of the outcome labels behind this finding preserves it under corrected labels and reveals that text-similarity methods are insufficient for distinguishing editorial from substantive regulatory change. Together, these findings locate the equity asymmetry upstream of agency response: in which rulemakings and obligations different commenter populations have the capacity to identify, interpret, and contest. Obligation-level auditing makes this upstream inequity visible.
Julian Flake, Christian Henke, Alessandra S. Kuntz, Matthias Löbe, Harshvardhan J. Pandit, Christian Draeger
Finding, sharing, and reusing data requires common interoperable vocabularies to adequately describe machine-actionable data policies. Semantic Web standards are a proven way to express such information describing data access and reuse. Narrative descriptions get a stable and agreed meaning by formal definitions of terms that have a globally unique identifier. The Data Privacy Vocabulary (DPV) represents a community standard for privacy, data protection, and related topics. DPV uses modular namespaces to define jurisdictions, such as "EU", and to represent concepts from laws within that jurisdiction, such as the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS). In this work, we present the creation of a DPV extension representing the German Health Data Use Act (Gesundheitsdatennutzungsgesetz (GDNG)) that has been set into force in March 2024. The GDNG regulates sharing and using health data for secondary use, such as research purposes and policymaking. It builds the German legal foundation for the future EHDS as the European initiative and regulation providing rules for sharing of health data within Europe. The parallel development of the EHDS and GDNG extensions resulted in many shared concepts, which contributed to an overarching extension: the DPV sector health extension. It sets the foundation of aligned privacy terms for future EHDS member states looking to build a national vocabulary aligned with the EHDS. All extensions were released as draft extensions with the latest DPV version 2.3, released 25th of February 2026.
Md Zubair, Hao Zheng, Grayson W. Armstrong, Lucy Q. Shen, Gabriela Wilson, Yu Tian, Xingquan Zhu, Min Shi
Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on certain demographic groups causing unfair performances. The two aspects can influence each other, as different data modalities may favor respective groups during optimization, leading to both imbalanced and unfair multimodal learning. This paper proposes a novel approach called MultiFair for multimodal medical classification, which addresses these challenges with a dual-level gradient modulation process. MultiFair dynamically modulates training gradients regarding the optimization direction and magnitude at both data modality and group levels. We evaluate MultiFair on three real-world medical classification datasets with diverse demographic attributes,including multiclass classification and missing-modality settings. Experimental results demonstrate its effectiveness.
Much of the manipulation in influencer content is not false but disproportionate, framing accurate facts as historic, urgent, or catastrophic. Existing approaches either verify factual claims or detect clickbait and propaganda in single-platform, single-language text; however, they overlook where such framing originates, because they treat a post and the replies beneath it as one unit. To address this gap, we measure rhetorical distortion with one instrument applied identically across six platforms and two languages, classifying creator posts and their replies separately. Our three-tier pipeline detects five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion): bilingual lexical rules propose candidates, negative filters cancel known false positives, and an LLM verifies or relabels the candidates. This design keeps labels transparent and comparable across platforms, calls the LLM on only 14% of posts, and supports validation against human annotators and a second model. On 13,060 posts from 330 accounts on RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter, platform is far more strongly associated with account-level distortion than content category (partial $η^2=0.49$ vs. $0.09$), and X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). On X/Twitter, the top-5 replies under a post carry 3.5x the distortion of the tweet they answer (1.5x at equal length), while creator tweets alone are indistinguishable from YouTube and Bluesky; two human annotators and GPT-4o confirm this gap. Measured distortion on threaded platforms thus lives largely in the audience layer, which calls for measuring creator and reply text separately.
Paschalis Bekos, Billy Tsouvalas, Nick Nikiforakis, Michalis Polychronakis
AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving personal and intimate information shared under an expectation of privacy. Yet, on the web application side, these platforms have the ability to embed third-party analytics, advertising, and session replay scripts that operate silently alongside the chatbot, creating an underexamined channel for conversational data leakage towards third parties. We conduct the first systematic large-scale measurement study of contextual leakage in AI chatbots, auditing the web interfaces of 42 general-purpose and domain-specific (health, legal, and education) platforms. Beyond standard network interception, we instrument the browser at the JavaScript engine level to attribute each data flow to the exact user action that triggered it, and the DOM capture mechanism used to retrieve its content, a granularity invisible to network-layer analysis alone. We find that 21 of these platforms leak conversation titles to third-party trackers, while nine leak verbatim user prompts. Beyond content, we observe a substantial amount of chatbot platforms sending user emails to third-party trackers, enabling the association of conversational context with a user's identity. Our findings cast traditional web privacy issues under a new light, highlighting the privacy issues that are inherent in how users interact with modern AI chatbots.
Disaster research in high-income settings shows that better-resourced communities see population inflows and faster recovery, while disadvantaged communities suffer prolonged population loss. Whether this pattern holds in rapidly urbanizing cities of the Global South remains unclear. Here we show, using mobile phone data from Pune, India, and Bandung, Indonesia, how neighborhood-level changes in phone presence, a proxy for short-term population movement, relate to flooding and neighborhood deprivation. We combine satellite-derived flood maps with phone records to compare flooded and non-flooded areas over the 30 days around each flood. In both cities, less deprived neighborhoods see phone presence rise after flooding, while more deprived neighborhoods see it fall, consistent with resource-seeking interpretations documented in North American and European disaster research. The cities differed, however, in how deprivation relates to flood exposure: in Pune, more deprived flooded neighborhoods showed suggestive evidence of disproportionately larger decreases in detected device presence than similarly deprived non-flooded neighborhoods; no comparable interaction was observed in Bandung. Economic considerations are associated with post-flood device-presence changes in South and Southeast Asian cities in ways that parallel high-income contexts, though local context adds variation.
Iulian Lucau, Adelin-George Voicu, Simon Munk Christensen
Generative AI (GenAI) has spread unevenly across Europe, and it is unclear whether higher use brings higher trust. This paper compares Denmark and Romania, the EU's highest and lowest GenAI users, and asks whether the difference in public and institutional trust between them corresponds to the difference in trust in AI. Drawing on trust, technology-acceptance and digital-divide theory, it is a descriptive, contrasting-case analysis of harmonised data: GenAI usage comes from Eurostat, trust in AI more generally from the KPMG/Melbourne global study and public and institutional trust from the Standard Eurobarometer. In 2025, 48.4% of Danes aged 16-74 had used GenAI in the past three months, against 17.8% of Romanians. Use falls with age and rises with education in both countries, and the relative gaps are far larger in Romania; the patterns remain visible within the published age-by-education categories. Trust in AI does not follow usage: Romanians trust AI as much as Danes or slightly more, although Denmark leads by 28 to 45 percentage points on trust in government, parliament and the legal system. The Denmark-Romania comparison thus does not support a simple country-level correspondence between general institutional trust and trust in AI; in KPMG's pooled model, trust in AI is tied most closely to confidence in AI governance. The observed usage difference is more consistent with disparities in general digital skills and opportunities for work- and education-related use than with differences in trust. However, the available data cannot identify the independent effects of these factors. The results rest on two cases and non-identical constructs, and call for comparable microdata and disclosure experiments.