Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2607.01807v1 Announce Type: new Abstract: Young people consistently say they want authentic self-expression, less judgment, and more interpersonal trust on social media, yet they rarely manage to engage that way. My dissertation argues that the obstacle is normative rather than individual: how youth engage is governed less by personal choice than by platform norms, peer perception, and beliefs about how others behave. I take a social norms approach to youth social media design organized around three claims. First, platform norms constrain individual behavior, producing a pluralistic ignorance in which youth enact norms they privately reject. Second, design interventions are themselves shaped by existing norms, so whether a feature works depends on the environment around it, which means relational goals such as privacy must be treated as social norms rather than individual settings. Third, a societal norm about what ``social media'' is -- equating it with a few mainstream platform
arXiv:2607.01747v1 Announce Type: new Abstract: Cumulative-link ordinal regression models are an alternative approach for analysing ordinal data such as Likert items, which are widely used in Visualization (and other related fields like HCI, psychology etc.). There are many researchers who are strong proponents of this approach, as it makes less stringent assumptions about the data, compared to the more commonly used linear model or ANOVA. Yet, ordinal regression models have seen limited adoption. I posit that one possible reason for this might be due to the difficulty in visually representing the results from such models, and in communicating the key takeaways in an intuitive manner. I propose the use of (modified) Complementary Cumulative Distribution Function (mCCDF) plots to visualize the results of ordinal regression models, and demonstrate how the same takeaways that researchers present from analyses which treat ordinal data as metric can be easily communicated using mCCDFs.
arXiv:2607.01692v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) into educational technology threatens to reduce mathematical learning to mere answer generation. This paper presents a generative study, usability study, and 12-participant field deployment of AITutor, an interactive system that translates theoretical pedagogical mechanisms into concrete user interface features. We explore how junior-high students preparing for high-stakes exams (Zhongkao) interact with AI tutoring. Through mixed-methods triangulation (7,379 telemetry events, 8 contextual observations, 10 interviews), we reveal that students actively resist traditional Socratic dialogue under time pressure, repurposing "answer-first" shortcuts as vital diagnostic checkpoints. We demonstrate how features like layered worked examples, step-linked visual grounding, and metacognitive scaffolding lower the interaction cost of reasoning repair. We contribute a "Reasoning-Centered Product Loo
arXiv:2607.01618v1 Announce Type: new Abstract: While ubiquitous computing research has explored diverse devices for personal health tracking, we know less about multi-device designs for family informatics, where health management is inherently collaborative. To understand how families adopt and perceive ubiquitous access to shared health data across contexts, we evaluated smartwatch-only, home display-only, and combined designs for tracking moods and goals, domains central to family health behavior regulation. 44 people across 12 families alternated between these designs over nine weeks. Log analysis revealed that mood tracking and goal reporting were significantly more frequent with the home display present compared to smartwatch-only use, despite an overall decline in mood tracking over time. Tracking peaked in afternoons, dropped on weekends, and occurred 2.6X more at home, with children tracking more consistently than adults across all designs. From interview analysis, we learned
arXiv:2607.01593v1 Announce Type: new Abstract: Affective visualization is increasingly studied in visualization research, yet how designers bring emotions into their visualization work remains unexplored. This paper addresses this gap through semi-structured interviews with 15 visualization practitioners. Using hybrid thematic analysis, we identify: (1) three functions that emotions can serve for viewers (entry, engagement, outcome); (2) three facets of how designers work with emotion (data, design, audience), along with design strategies; and (3) ethical considerations in the design process. We also observe that affective intent often emerges during the design process rather than being planned from the outset, and that emotional impact arises from accumulated design choices rather than isolated visual elements. Finally, we highlight evaluation as a key challenge identified by our participants.
arXiv:2607.01588v1 Announce Type: new Abstract: Extended Reality (XR) has become an important interaction paradigm in Human-Computer Interaction (HCI). XR studies are used to investigate interaction, perception, and user behavior in immersive environments, and typically involve experimental tasks, 3D scenes, and interactive logic. However, turning an initial XR study idea into a runnable prototype remains fragmented across study design, scene construction, and interaction implementation. We present OrchestrXR, a multi-agent human-AI workflow for early-stage idea-to-prototype XR study authoring. Rather than treating XR study creation as one-shot generation, OrchestrXR supports a controllable workflow across study design, scene generation, and interaction generation through structured schemas, multi-agent orchestration, and interactive human-agent interfaces, producing a Unity-based prototype from a researcher's idea. A user study with 12 XR researchers suggests that OrchestrXR provides
arXiv:2607.01506v1 Announce Type: new Abstract: As Artificial Intelligence (AI)-based technologies have been integrated into school classrooms where multiple stakeholders (with different roles) interact with each other, it is critical to deeply understand stakeholder views in the classroom. In particular, prior work has not fully uncovered how teachers' and school students' views might or might not align well with each other, especially in K-12 classrooms. We conducted a speed-dating study using storyboards with 16 school students and 15 school teachers in Germany to investigate alignments and misalignments between their views on student-AI decision-making control in K-12 classroom. Through an explicit pair-matching analysis, we found that students and teachers had misaligned views on several key topics, including how much they trust AI and social and emotional aspects of student learning with AI. Findings also revealed the importance of teacher-student relationships outside of AI use
arXiv:2607.01359v1 Announce Type: new Abstract: Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embodied pedagogical approach, in which viewers observe the dynamic hand-drawing of a visualization, can mitigate confirmation bias and improve interpretation accuracy. We conducted a study comparing static bar charts to videos in which charts are constructed through hand-drawing, across contexts that either align with or challenge participants' prior beliefs. The results indicate that hand-drawn videos helped participants accurately interpret data, even when the data conflicted with their prior beliefs. This approach also reduced belief-consistent errors and increased belief-overriding responses. These findings suggest that exposing the construction process of a visualization supports more accurate reasoning and mitigates the influence of confirmation bias. Conseque
arXiv:2607.01242v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference module that jointly interprets query text and keystroke dynamics via in-context LLM prompting, and (ii) an expertise-conditioned response generation that adapts the level of detail, terminology, and conceptual complexity. Our user study with 40 participants and 1270 queries demonstrated that ExPerT reduced expertise inference error by 65.7% compared to the strongest baseline (MAE = 0.398 vs. 1.162) and improved response satisfaction by 17.52% (from 3.71 to 4.36) on a 5-point Likert scale.
arXiv:2606.22645v2 Announce Type: replace-cross Abstract: Large language models have substantially improved information retrieval and question answering; however, existing datasets generally support either vector-based retrieval over unstructured text or reasoning over knowledge graphs, without providing a unified representation that combines both paradigms. Moreover, current benchmarks rarely provide ground-truth entities, relations, and fact-grounded question-answer pairs aligned with the underlying corpus. To address this gap, we introduce All Relations Lead to Rome (ARLtR), a unified framework for automated knowledge graph construction and fact-grounded question-answer generation. ARLtR jointly constructs a knowledge graph, embeddings, and question-answer pairs that are explicitly grounded in extracted entities, relations, and supporting textual evidence. We further instantiate the framework as a historical dataset centered on the Roman Empire, comprising over 19,000 entities, 16,0
arXiv:2605.02800v2 Announce Type: replace-cross Abstract: The overreaches of mainstream social media platforms have been extensively reported and studied. For activist communities, these platforms pose risks of surveillance, censorship, or erasure. Decentralized social networks (DSNs) serve as alternative online spaces that appear to prioritize values such as user privacy, free speech, and community control. However, the decentralized ecosystem is vast and complex, making it difficult for communities to understand how to best use these platforms for their organizing aims. We address this gap by proposing a conceptual framework for navigating the DSN landscape that defines core activist community needs -- minimal overhead, community building and reach, on- and offline safety, and operational sustainability -- and links them to concrete platform affordances such as resource efficiency, interoperability, and data ownership. We apply the framework to (1) evaluate and compare the sociotechn
arXiv:2605.25416v2 Announce Type: replace Abstract: While substantial efforts in anti-trafficking research and practice have focused on identifying and assisting victims after exploitation occurs, comparatively less attention has been paid to preventing victimization at the recruitment stage. Although some platforms offer preventive tools, such as background checks triggered by in-person meeting detection, these measures primarily protect potential victims rather than directly limiting traffickers' recruitment activities. In this paper, we propose a computational framework to identify human trafficking recruiters through their linguistic features and to characterize their online recruitment patterns. We introduce a network-driven labeling method to construct large-scale ground truth for trafficking-at-risk job advertisements. Our results reveal significant linguistic differences between safe and risky advertisements and demonstrate that language models and embedding representations beh
arXiv:2605.01549v2 Announce Type: replace Abstract: The scaling-law era has transformed artificial intelligence (AI) from research into a global industry, but its rapid growth also raises concerns over energy usage, carbon emissions, and environmental sustainability. Unlike traditional sectors, the AI industry still lacks systematic carbon accounting methods that support large-scale estimates without reproducing the original training process. This leaves open questions about how large the problem is today and how large it might be in the near future. Given its central role in hosting open-source AI models, the Hugging Face (HF) platform provides a large-scale and publicly accessible corpus for carbon accounting. We estimate aggregate training emissions of HF open-source models using available emissions, energy, compute, and model metadata. To address uneven disclosure quality, we introduce a tiered approach to handle incomplete metadata, supported by empirical regressions that assess e
arXiv:2604.23065v2 Announce Type: replace Abstract: Frontier AI developers are increasingly deploying highly capable models internally to automate AI R&D, but these deployments currently face limited external oversight. It is essential, therefore, that developers provide evidence that internally deployed models are safe. While recent work has highlighted the risks of internal deployments and proposed broad approaches to transparency and governance, there remains little guidance on the specific information developers should disclose about them. We address this gap by identifying key information that companies should disclose about internally deployed models across four categories: capabilities, usage, safety mitigations, and governance. For each category, we analyse the key benefits and limitations of disclosure and consider how disclosure-related risks can be mitigated. Our framework could be used by developers to inform both public transparency documents, such as model system cards, a
arXiv:2604.21412v3 Announce Type: replace Abstract: Incident monitoring can drive safety improvements in high-reliability industries and population-scale technologies, but remains underdeveloped in AI governance. Public databases catalog thousands of AI incidents, but simple incident counts conflate media reporting propensity, system deployment ("exposure"), and harm frequency per unit exposure. We propose a methodological framework that accounts for these factors and calibrates confidence to available evidence in analyzing how AI incidents change over time. The framework comprises three components: a structured monitoring question that defines the scope of the analysis; a tiered estimation process that separately derives harm and exposure trends, including through LLM-assisted filtering of public incident databases; and a classification scheme that maps the resulting trend estimates onto actionable governance categories (Escalating, Mitigating, Concentrating, Receding or Unclassifiabl
arXiv:2604.20870v2 Announce Type: replace Abstract: Despite growing interest in AI education, most AIED initiatives remain narrowly targeted toward STEM-prepared students, limiting participation by non-STEM learners and adults seeking to engage with AI in public-interest, policy, or workforce contexts. This paper presents and evaluates an NSF-funded, innovative mixed-cohort AI education model that intentionally integrates non-STEM undergraduates and adult learners into a shared learning environment centered on ethical reasoning, socio-technical judgment, and applied AI literacy rather than technical proficiency alone. Drawing on mixed-methods data from course surveys, open-ended reflections, and educator reports, we examine learners' academic agency, confidence navigating AI concepts, critical engagement with ethical tradeoffs, and perceived expansion of postsecondary and career trajectories. Quantitative results indicate significant gains in confidence and perceived relevance of AI ac
arXiv:2601.14534v2 Announce Type: replace Abstract: The United States labor market has entered a period in which high job vacancy rates and prolonged unemployment persist together. Classical theory attributes such conditions to skills mismatch or geographic immobility, but neither fully explains a pattern now widely reported: qualified candidates are rejected at the earliest, automated stage of hiring, before any human sees their application. This paper introduces Artificial Frictional Unemployment (AFU), a framework describing how deterministic automated screening rejects qualified candidates through semantic misinterpretation rather than genuine skill gaps. We situate the phenomenon within labor economics and information asymmetry theory and formalize the mechanism by which legacy Applicant Tracking Systems (ATS) turn hiring into a high-precision classification problem that inflates false negatives. The contribution is primarily conceptual. To make the mechanism concrete, we report a
arXiv:2508.05953v2 Announce Type: replace Abstract: Using Large Language Models (LLMs) to give educational feedback to students for their assignments has attracted much attention in the AI in Education (AIED) field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In this paper, we introduce a synthetic computer science university assignment dataset for LLM-based educational feedback research, called SCALEFeedback (Synthetic Computer science Assignments for LLM Educational Feedback Research). The dataset is generated via Sophisticated Assignment Mimicry (SAM) framework specifically designed to synthesise this dataset and that utilizes one-to-one LLM-based imitation from real assignment descriptions, rubrics, a
arXiv:2508.05952v2 Announce Type: replace Abstract: Large language model (LLM) tutors are increasingly used to generate educational feedback, but existing research has focused mainly on feedback generation rather than feedback evaluation. As a result, LLM-generated feedback may offer limited pedagogical value and carry risks of hallucination. The current study introduces DeanLLM, an automated review framework for comprehensively evaluating feedback generated by LLM tutors before it is shared with students. We developed a 16-dimension evaluation framework covering feedback content, educational effectiveness, and hallucination risks, and validated it using using human-expert annotations of LLM-generated tutor feedback on synthetic computer science assignment submissions derived from real coursework. We then examined whether LLMs could serve as automated LLM-generated tutor feedback reviewers, and used the best-performing reviewer to benchmark tutor feedback generated by 10 commercial LLM
arXiv:2607.02432v1 Announce Type: cross Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation. This paper evaluates whether four frontier Large Language Models (GPT, Claude Opus, Gemini, and GLM) can approximate expert judgment when grading short Linux/bash command responses. The study adopts a four-level cognitive taxonomy that combines cognitive complexity and operational impact, ranging from information retrieval (L1) and basic file manipulation (L2) to structural operations (L3) and advanced system management (L4). The models were tested with two prompt variants, a minimal baseline and a rubric-enhanced version, on 1200 real responses from second-year Computer Engineering students independently graded by three expert instructors. Gemini~3.0 Pro with rubric-guided prompti
arXiv:2607.02245v1 Announce Type: cross Abstract: Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users' dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. Our architecture introduces three technical innovations: (1) a multi-source assessment framework integrating self-reported, physiological, and contextual data to mitigate algorithmic bias; (2) valence-arousal emotion mapping using Russell's Circumplex Model of Affect to route users to specialized AI agents; and (3) dual-mode intervention delivery combining conver
arXiv:2607.02181v1 Announce Type: cross Abstract: Americans' warmth toward members of the opposing political party has fallen sharply over the past three decades -- yet meaningful cross-partisan contact remains scarce, in part because people actively avoid it. Across five preregistered studies (total N = 3,960 U.S. partisans), we test whether brief conversations with AI chatbots representing the political outgroup can substitute for the contact people shun. Synthetic contact first lowers the barrier to entry: partisans would endure almost twice as long contemplating their own mortality to avoid a human outgroup partner as an AI one. These conversations then correct the misperceptions that fuel division. At baseline, Democrats placed Republicans more than a standard deviation past their actual position on environmental consumption attitudes -- enough to flip the average Republican from supportive to opposed -- and a single ten-minute conversation with an outgroup chatbot corrected those
arXiv:2607.02049v1 Announce Type: cross Abstract: Large Language Models are increasingly deployed in emotional-support contexts and crisis-related situations. Nevertheless, their cross-lingual abilities in these circumstances remain underexplored. Existing benchmarks emphasize multilingual performance but rarely examine crisis-related empathy and cultural grounding in low-to-mid-resource languages. We introduce SPLIT, a 500-prompt benchmark designed to evaluate LLM consistency in generating emotionally grounded responses across five categories: Stress, Panic, Loneliness, Internal Displacement, and Tension. We evaluate three technically diverse LLMs across three dimensions: Empathetic Accuracy, Linguistic Naturalness, and Contextual & Cultural Grounding. The framework aims to assess and compare the quality of LLM responses in both English and Ukrainian languages, as well as to explore the reliability of the LLM-as-a-jury paradigm. Our findings reveal that Gemini-2.5-Flash and LLaMA-3.3-
arXiv:2607.01833v1 Announce Type: cross Abstract: The global development of Library and Information Science (LIS) is influenced by various factors such as the economy, society, culture, discipline, tradition, and more. Consequently, the research methods of LIS vary greatly among countries. To better understand these differences, we conducted a study of 5,281 research papers from 81 countries published in internationally representative journals over the past thirty years. We manually annotated the research methods used in some articles through content analysis, and subsequently developed and trained a deep learning model for automatic classification of research methods. Using this method, we conducted a comparative analysis of the usage of research methods in different countries. Our findings reveal that there are differences in the research methods used across countries, with each country having its unique research profile and distribution of research methods. Even when investigating t
arXiv:2607.01828v1 Announce Type: cross Abstract: Research in the social sciences has shown that there are gender differences in the selection of research methods, with women often opting for qualitative methods while men prefer quantitative methods. However, it is important to consider that research methods are generally chosen based on the research topic. To figure out the influence of gender on research method selection, a study was conducted in the field of Library and Information Science, using a more fine-grained method classification system and an automatic classification model called CogFT, which is based on full-text cognition. The findings showed that women tend to use Interview while men prefer Theoretical approach, across a range of topics. The study offers insights into the specific research design processes that contribute to gender differences in method selection and suggests ways to promoting gender inclusivity and equality in academia by considering research method use
arXiv:2607.01730v1 Announce Type: cross Abstract: Liquid democracy promises to improve collective decision-making by allowing voters to vote directly, delegate their voting power to trusted participants, or combine both approaches through fallback mechanisms. However, existing deployments typically rely on transparent delegation, which exposes voters to popularity-driven herding, makes coercion verifiable, and introduces systemic fragility when highly-backed delegates abstain. In this paper, we propose a secure liquid democracy mechanism that resolves the tension between informed expertise routing and systemic robustness. We introduce a sealed delegation regime using decentralized timed-release encryption, which cryptographically hides delegation choices during the formation phase to prevent herding and coercion, while restoring full public auditability for the final tally. To address delegate failures, we extend the protocol with ranked multi-delegation and personal fallback ballots.
arXiv:2607.01245v1 Announce Type: cross Abstract: We introduce Office Comprehension Bench (OCB), the first public benchmark to jointly evaluate LLM systems on Word, Excel, and PowerPoint comprehension over native file formats (.docx, .xlsx, .pptx) and their variants. OCB consists of two tracks. File Fidelity Q&A tests structural and visual perception of office artifacts - tables, charts, embedded images, formulas, and app-specific elements such as headers, speaker notes, and named ranges. Domain Q&A tests expert-level reasoning grounded in real-world industry documents across 12 professional domains, with queries requiring multi-step analysis and synthesis across documents. Each reference answer is decomposed into atomic, binary-gradable claims, and an ensemble of LLM judges scores responses against each claim independently. Even the strongest frontier system in its default reasoning mode reaches only about 59.3% on Domain Q&A increasing thinking depth within a tier does not move perfo
arXiv:2607.01244v1 Announce Type: cross Abstract: The growing number and complexity of technical regulations represent an important challenge for all professionals in regulated industries. This paper describes a case study, from design to deployment, of building a Retrieval-Augmented Generation system for the consultation of complex technical regulations in the railway domain. Although developed for the railway sector, this testimony of an industrial experience is of particular value for technical domains where regulatory compliance and accurate information retrieval from complex documentation are essential requirements. It also constitutes a human-centered approach for implementing LLM-powered technical documentation consultation across various regulated industries, balancing technological capabilities with domain expertise.
arXiv:2607.02467v1 Announce Type: new Abstract: Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding (i.e., lower error than) the market itself. Collaborative traits (perspective-taking, intellectual humility, and curiosity) rather than raw cognitive ability or model benchmarks, distinguished who reached that mode. The results are preliminary but statistically robust, and motivate a pre-registered replication now
arXiv:2607.02313v1 Announce Type: new Abstract: As conversational AI systems become more deeply integrated into daily life, the implications for human agency are increasingly urgent to understand. AI's potential to amplify capability sits alongside risks of individual and collective disempowerment, yet empirical, ecologically-valid evidence about cumulative usage is scarce. We analyze deep ethnographic data from a study of daily AI chatbot users (n = 51) in the United States, Germany, and Singapore to illuminate conversational AI usage in situated context as a sociotechnical practice. We show that people consistently link sustained AI usage to perceived gains in individual agency. Crucially, these perceived gains often outweigh concerns about accuracy, reliability, and consistency to shape usage patterns. Our findings challenge prevailing assumptions about how and why humans use AI systems over time, suggesting that traditional trust-based models are not sufficient for explaining human
arXiv:2607.02201v1 Announce Type: new Abstract: The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed. At least 74 AI risk taxonomies exist, and almost all stop at the catalog. The hard part of auditing is not naming a risk but operationalizing it: turning it into a test run against a real system, a measured value, a calibrated severity, and a defensible grade. This paper leads with that bridge. We present the operationalization layer Eticas has built and run, shown end to end on a single risk (PII leakage) against a public benchmark, and then the open taxonomy that makes the method scale. On GPT-4-0314, a disclosure risk that seven external frameworks require be controlled is measured at 0%, 51%, and 84% disclosure as adversarial conditioning increases, mapping through calibrated severity bands to a
arXiv:2607.02197v1 Announce Type: new Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.
arXiv:2607.02144v1 Announce Type: new Abstract: While AI promises major benefits, its development and deployment can shift costs onto others, including environmental pressures on local communities, labor and creative displacement, and systemic risks from rapid frontier development. Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms. In this paper, we explore the viability of AI taxation. More broadly, AI taxation should not be understood only as Pigouvian correction. In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity. We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities. We further assess the benefits a
arXiv:2607.01913v1 Announce Type: new Abstract: Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known at design time. This technical report reframes strategic red teaming as a board-level governance discipline for testing the assumptions under which AI-enabled strategies are approved, funded, and supervised. The report proposes a six-component model for strategic red teaming in AI governance: an explicit assumption register, an adversarial mandate, independence criteria, evidence grading, a board-facing decision record, and a follow-up mechanism for unresolved findings. The model is intended to make strategic uncertainty inspectable before it becomes operational exposure. It treats red teaming not as penetration testing, scenario theatre, or generic risk review, but as structured adversarial testing of the claims on which governance decisions depend. The contribution is conceptual and design-
arXiv:2607.01776v1 Announce Type: new Abstract: In the age of AI, what will be good knowledge? This article, which is accepted and forthcoming in a special issue of Modern Fiction Studies on "Cultural AI" in 2027, applies digital humanities methods to map epistemic virtues (like "true," "accurate," "creative") used in a corpus of 553 journal articles on AI published in 2024. "Creativity" comes in for special attention as an example. Exploring this discourse of value, the article considers how a framework might be developed for evaluating the knowledge-worth of AI -- one less locked into values formed around pre-AI "knowledge work" agents or structures, and more open to the future values of "generativity." The essay is supported by an online digital kit for exploring data models of the corpus of articles on AI it studies.
arXiv:2607.01750v1 Announce Type: new Abstract: Open source software (OSS) is not homogeneous. A project's purpose, governance, and funding shape how its community forms, who contributes, and how the software is maintained, yet empirical research often samples OSS broadly and reports findings as if they held for open source as a whole. We argue that OSS comprises distinguishable sub-genres, and that the sub-genre a study samples bounds how far its findings generalize. Using a light, multi-source review that screens 3,925 unique papers, we synthesize a typology of fourteen OSS sub-genres, from well-studied ones such as community-driven, company-backed, foundation-governed, research and scientific, and open source for social good (OSS4SG), to under-studied ones such as multi-company co-opetition, protestware, and open-source appropriate technology. We place the sub-genres in a framework that records each one's primary driver, governance, and funding, with its maturity in the literature a
arXiv:2607.01460v1 Announce Type: new Abstract: Human-annotated data remains foundational for machine learning and social media analysis. However, traditional data collection often relies on cumbersome pipelines that isolate content from its original source, compromising ecological validity. To address these challenges, we present Social-Annotate, a flexible browser extension that facilitates direct data collection on online platforms. By injecting customizable forms into webpages, the tool captures annotations while users interact with the native environment. Social-Annotate offers no-code design interface for the survey forms for non-technical users. Since injecting custom elements directly into host platforms creates a brittle dependency on evolving interfaces, we integrate a self-healing agent powered by large language models. This automated pipeline autonomously detects structural changes, regenerates valid target selectors, and validates them within a live browser environment. Ou
arXiv:2607.01258v1 Announce Type: new Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software. In particular, graphics cards have become central to AI training, with frequent hardware updates required to meet escalating computational demands. However, the environmental damages of graphics cards production remain understudied. This study addresses this gap by estimating the environmental damages associated with graphics cards production over the past decade (2013-2025). We analyze trends in energy consumption, carbon emissions and resource depletion. We compile and provide a dataset documenting the environmental damages of NVIDIA workstation graphics cards production since 2013. Our analysis of this dataset reveals a steady increase in production-related impacts over the period. Our finding highlights the need for
arXiv:2607.01257v1 Announce Type: new Abstract: The rapid digitalisation of financial systems has improved operational efficiency and financial inclusion while simultaneously increasing exposure to sophisticated forms of cyber-enabled fraud and electronic financial misconduct. Conventional auditing systems, which largely depend on retrospective verification and rule-based monitoring, increasingly struggle to address the complexity and speed of modern financial crime. Consequently, financial institutions are progressively adopting Artificial Intelligence (AI)-enabled Accounting Information Systems (AIS) and Natural Language Processing (NLP) technologies to strengthen fraud detection, continuous auditing, and institutional monitoring. This study examined the influence of AI-enabled AIS on auditing and fraud detection effectiveness within Nigeria's financial services sector while additionally evaluating the moderating role of NLP. Anchored on the Fraud Diamond Theory and the Technology Ac
arXiv:2607.01256v1 Announce Type: new Abstract: Overwhelmed courts in the United States review millions of default judgments each year. Unfortunately, such manual reviews are time-consuming and prone to error. In an audit of 188 debt collection cases granted default judgment by the Superior Court of Los Angeles, we find that 4% contained major defects that should have entirely prevented default judgment, 10% contained inconsistencies requiring reduced judgments, and 32% contained errors requiring amendment prior to judgment. To support courthouses in default judgment review, we collaborated with courthouse attorneys and judges in designing a Default Assistant. The Default Assistant employs large language models to evaluate a case with respect to predetermined legal requirements and provide cited recommendations for an expert user's review. We equip users to verify these recommendations by grounding the assistant's explanations in cited quotes and tables from the original case filings.
arXiv:2607.01255v1 Announce Type: new Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far, the dominant reaction has been defensive, this is, most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort, prioritizing basic training for academic staff over students. Other group of pioneering universities is doing the opposite, pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold. This paper sides with that second view. Obsessing about detection is a dead end, since generated text is increasingly hard to distinguish from human writing, and detectors still misfire too often to be trusted. What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use, redesign assessment toward authentic and interdisciplinary assessment that foste
arXiv:2607.01254v1 Announce Type: new Abstract: Benchmarks are the primary instruments through which AI capability is measured, compared, and governed. This paper argues that the validity of frontier AI benchmarks is a function of the quality of human judgment embedded in their construction, and that this quality is structurally scarce in ways that standard scaling narratives obscure. As foundation models approach ceiling performance on existing evaluation suites, discriminating signal concentrates in the hardest benchmark items, precisely those requiring elite expert judgment to design. We term this the benchmark ceiling problem: the progressive exhaustion of evaluation signal as models saturate the easy majority of items while the difficult tail, authored by a thin stratum of highly expert evaluators, remains the only source of genuine discrimination. The paper develops this argument in three steps. First, we present a formal model of benchmark signal depreciation. Benchmark scores a
arXiv:2607.01253v1 Announce Type: new Abstract: Rationale. The diagnostic radiologist's role in 2035 will not look like it does today. Imaging AI is already changing how worklists are organized, how reports are generated, and which cases require a radiologist's attention. What remains genuinely contested is not whether the role changes but how. Approach. Three subject-matter experts (two radiologists and one health tech professional with more than 20 years of experience in medical imaging IT) independently authored 2035 job descriptions for the diagnostic radiologist using a shared template. Each author wrote from a distinct vantage point: one optimistic, one framed as a trade-off view incorporating workforce economics, and one structured around professional stratification. The three versions were published openly and subjected to a structured comparison across seven dimensions. Key findings. The three versions agree on direction but disagree on magnitude. All three describe a radiolog
arXiv:2607.01252v1 Announce Type: new Abstract: Background: Dermatology AI has mainly focused on image-based diagnosis, while chronic disease workflows have received less attention. We surveyed Indian dermatologists to map routine clinical challenges, with a focus on atopic dermatitis (AD), and assess current AI use. Methods: A nationwide cross-sectional survey commissioned by the Society for Eczema Studies included 377 practicing Indian dermatologists. The survey assessed clinical challenges, AD workflow barriers, AI use, adoption barriers, and ethical concerns. Analyses used descriptive statistics, chi-square tests, false discovery rate correction, and multivariable logistic regression. Results: Patient adherence (61.3%) and treatment planning in difficult or refractory cases (57.0%) were reported more often than diagnostic uncertainty (48.0%). In AD care, severity scoring was reported as a challenge by 47.7% and had the lowest satisfaction among measured workflow areas. Current AI u
arXiv:2607.01251v1 Announce Type: new Abstract: Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: models are incentivized to be persuasive to the judge, which may not always align with epistemic honesty. In this work, we propose an alternative paradigm: disagreement resolution, which reframes the interaction mechanism from adversarial debate to collaborative truth seeking. Drawing on principles from human mediation and conflict resolution, where mediators facilitate dialogue to help disputing parties reach consensus rather than adjudicating between them, we design an automated pipeline that adapts these strategies to AI oversight. Unlike standard debate where models argue for fixed positions, our pipeline directs models to collaboratively identify points of disagreement, examine the evidence for conflicting claims, and converge toward consensus or isolate the specific ''crux'' of their dis
arXiv:2607.01250v1 Announce Type: new Abstract: Sociotechnical alignment concerns the social desirability of AI behavior and is thus inherently normative, not merely technical. While NLP research increasingly addresses its technical aspects, it often leaves underspecified what such "social desirability" entails. We argue that this reflects a fundamental gap: the absence of a systematic way to specify how sociotechnical alignment defines, justifies, and evaluates socially desirable AI behavior. To address this gap, we introduce a human-centered framework for specifying sociotechnical alignment. We draw on social-scientific accounts of sociobehavioral desirability to ground the basis for behavioral desirability judgments and use this framework to analyze how alignment is specified in practice. Our systematic literature review identifies recurring patterns: normative concepts grounding desirability judgments are often unspecified or conflated with alignment targets for (desired) system be
arXiv:2607.01248v1 Announce Type: new Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these settings, users may obtain highly structured answers, plans, and judgments without sufficient domain practice. This paper proposes a practice auditing framework for LLM use and AI-generated content governance. It introduces collective empiricism to describe how LLMs compress and reorganize large-scale human experience into outputs that appear empirical and rational, and pseudo-rational cognition to describe how users may mistake AI-generated structured expression for their own rational understanding. The paper analyzes AI subjectivity illusion, subjectivity structures in input materials, template loops in AI-AI conversations, statistical misjudgment in AIGC detection, and memory pollution when generated content enters future contexts, long-term memory, retrieval spaces, or agent skill systems. To r
arXiv:2607.01247v1 Announce Type: new Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps. They are also difficult to grade at scale because instructors must apply partial-credit rubrics consistently while giving feedback that helps students repair misconceptions. This paper evaluates six contemporary large language model (LLM) configurations, Gemini 3.1 Pro Extended, Gemini 3.5 Flash, ChatGPT 5.5 Pro Extended, ChatGPT 5.5 Thinking, Claude Pro Opus 4.7, and Claude Sonnet 4.6, as grading assistants for an undergraduate discrete mathematics examination. The study compares two grading policies. The BASELINE policy uses a stricter rubric-following prompt that emphasizes explicit evidence and complete justification. The LIBERAL policy was added after preliminary grading showed that the baseline condition sometimes applied harsh point deductions and failed to recognize valid parti
arXiv:2607.01246v1 Announce Type: new Abstract: Recent proposals for HTTP-based sustainability disclosure focus on \textbf{what} environmental information should be transmitted at the protocol boundary, for example through response headers, but leave open the practical question of \textbf{how} such per-request values can be generated in realistic deployments. This paper addresses that implementation gap. We present a model-based approach for estimating resource consumption and $CO_2e$ per HTTP request without requiring fine-grained production power telemetry. The approach benchmarks endpoints offline under controlled conditions, derives compact endpoint-specific energy models from observable request features, and evaluates these models online at the HTTP server boundary. We implement this mechanism as an nginx extension that loads a JSON model registry and emits per-request metadata for energy, grid intensity, embodied emissions, and total request-level impact. We show that heterogeneo
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