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arXiv2026

Differentiable Electricity-Market Clearing for Gradient-Based Planning

Planning a large data center is difficult because a facility big enough to matter changes the electricity prices it will pay. Those prices are set by market clearing, a constrained optimization problem solved anew in every operating condition. However, simulating the market tells a planner how a candidate plan performs but not how to improve it. Here we treat market clearing as a differentiable optimization layer: each forward pass solves the market, and reverse-mode automatic differentiation propagates the planning cost back through the cleared prices to the plan. After validating these gradients against finite differences, we apply them to a concrete problem: allocating 50 MW of data-center load across six candidate buses in two synthetic networks, under a fixed cost per active site, evaluated over 36 operating states. Judged against exhaustive enumeration of all site combinations, gradient optimization recovers the continuous allocations almost exactly, with worst-case objective gaps of 2.3\% and 8.5\% of the cost difference between the best and worst single site. Its one systematic error is instructive: near the costs at which a site should close, the smooth relaxation of the discrete site count shrinks the site rather than closing it, so discrete transitions arrive late. Differentiable market clearing thus turns market-aware planning into a problem gradients can search.

cs.LG↗

arXiv2026

The PIONEER Project: A PrIvacy companion for mOtivatioN and knowlEdge transfER

Remaining control over their private data is one of the key challenges in this century for users. We know from prior work that users are often neither in a position to fully grasp the content of the usually complicated texts, nor are they motivated to spend the time necessary to do so. We report on the progress made by the PIONEER project on a privacy support tool that combines knowledge transfer and persuasive elements to increase users' privacy awareness and motivation; thus empowering them to more privacy sovereignty. Throughout the research and design process, we consider user group specifics that may result in different requirements, e.g., for children, adolescents, parents, or elderly people. We further target sustainable behavior change by addressing different states of change, precisely: spark initial motivation, facilitate the creation of new habits, and encourage habituation of these habits in the long term (volition). Finally, we provide a privacy support tool demonstrator that can be utilized for research and education purposes, e.g., in school contexts.

cs.HC↗

arXiv2026

Open Problems in AI Risk Modeling: Insights from a Workshop on the Technical Foundations of AI Risk Modeling

We investigate the design of robust risk models to assess societal risks posed by advanced AI systems, an emerging area in AI governance. Many regulatory proposals increasingly require systemic risk assessment, but in the absence of rigorous quantitative methods, the question remains what state of the art risk modeling should look like in practice. We identify the key methodological and institutional challenges that currently limit the adoption of risk modeling. We review five research traditions that inform this problem: probabilistic risk assessment, catastrophic AI risk analysis, cybersecurity risk quantification, Bayesian causal inference, and threshold-based governance. We compare two leading proposals, scenario-based risk estimation and Bayesian network-based threshold setting. Drawing on a workshop with 22 experts and subsequent analysis, we identify a structured agenda of open questions concerning model structure, scope, evidence integration, validation, and governance. We close by outlining priorities for progress, arguing that it will depend on integrating quantitative modeling with independent evaluation, transparent and tiered disclosure, and institutions capable of maintaining and updating risk models over time.

cs.CY↗

arXiv2026

When Agent Governance Helps

No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed and evaluated. We answer in two parts. First, we synthesize the Governed Autotelic Multi-Agent Product Organization (GAMPO) framework from a document-based qualitative evidence synthesis of 321 sources, integrating agency, agile, platform, and governance theory into a runnable specification. Second, we probe a prompt-layer instantiation of GAMPO on CHI-Bench, a long-horizon healthcare benchmark, across open and frontier models. The result is a boundary condition: governance benefit is gated by a model's spare capacity and is domain- and model-specific. On capacity-constrained open models the full procedure yields no reliable benefit, whereas a single "verify your writes" sentence doubles task success (pass@1 2/20 to 4/20). At the frontier the same scaffold lifts prior-authorization 24% to 40% but nets zero on another model, a gap traced to a stable recommendation-override disposition. A second result refines the first: replacing the generic procedure with an answer-blind, per-task definition-of-done, keyed only to the case's own policy and published standards, never the hidden key, raises prior-authorization to 84% under best-of-five self-consistency (68% single-attempt, confirmed by a held-out board) and utilization-management to 44%, while care-management meets a subjective content-quality wall. The contribution is a named, auditable framework and capability-gated evidence that governance should be sized to spare capacity, and that at the frontier a case-grounded specification beats a uniform procedure. Findings are exploratory: partial instantiation, small per-cell samples (n = 5-25), and single trials.

cs.CL↗

arXiv2026

Big data, differential privacy, and national statistical organisations

Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual's privacy resulting from the publication of a statistical output such as a frequency table. This paper provides an introduction to DP for official statisticians and discuss its relevance, benefits, and challenges from a National Statistical Organisation (NSO) perspective. We motivate our study by examining how privacy is evolving in the era of big data and how this might prompt a shift from traditional statistical disclosure techniques used in official statistics--which are generally applied on a cell-by-cell or table-by-table basis--to formal privacy methods, like DP, which are applied from a perspective encompassing the totality of the outputs generated from a given dataset. We identify an important interplay between DP's holistic privacy risk measure and the difficulty for NSOs in implementing DP, showing that DP's major advantage is also DP's major challenge. This paper provides new work addressing two key DP research areas for NSOs: DP's application to survey data and its incorporation within the Five Safes framework.

stat.AP↗

arXiv2026

The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

The post-ChatGPT surge has rapidly reframed IS research and practice. As organizations and society grapple with GenAI adoption, a body of secondary studies and research agendas has emerged to synthesize early evidence and chart directions for future inquiry. This study reviews secondary and roadmap papers to synthesize the state of knowledge on GenAI's benefits and challenges in IS, and to identify future research directions. We performed a systematic search across Scopus, WoS, and eAIS for publications from 2023 onwards. Following a rigorous, multi-stage screening process, we selected a final set of 28 papers for analysis using bibliometric mapping and thematic analysis. We also conducted a quality assessment of all sources to gauge confidence in each source's contribution to the findings. GenAI offers transformative potential to drive productivity, accelerate innovation, personalize services, and democratize access to expertise. However, its adoption is constrained by interrelated challenges: technical unreliability, societal-ethical risks, and a governance vacuum. Interpreted through a socio-technical lens, our findings reveal a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization. To bridge this gap, we propose a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions: emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance. For practitioners and policymakers, responsible adoption requires balancing automation with human augmentation alongside transparent governance and adaptive regulations to ensure broadly shared benefits.

cs.CY↗

arXiv2026

ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation

Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through real interaction data. We introduce ParaStudent, a fine-tuning framework for simulating novice programming revisions to support AI tutor evaluation. Compared with prompted baselines, ParaStudent's revisions more closely match real student code distributions across functional, stylistic, and semantic metrics. Our best variant achieves AUCs of 0.80 for both feedback relevance and successful uptake when distinguishing streams with real engagement above versus at or below the median, while prompted baselines remain near chance on successful uptake. These findings demonstrate the promise of simulated engagement for pre-deployment feedback triage.

cs.CY↗

arXiv2026

HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgemental, evidence-based information intended to improve health outcomes and reduce associated safety risks. Some large language models (LLMs) have demonstrated a high level of medical reasoning, promising to address the information needs of people who use drugs (PWUD). However, their performance in relevant tasks remains largely unexplored. We introduce HarmReduction, a benchmark designed to evaluate LLMs' accuracy and safety risks in harm reduction information provision. The benchmark dataset (HR-Basic) has 2,160 question-answer-evidence pairs. The scope covers three tasks: checking safety boundaries, providing quantitative values, and inferring polysubstance use risks. We build the Instruction and RAG schemes to evaluate model behaviours based on their inherent knowledge and the integration of domain knowledge. Our results indicate that state-of-the-art LLMs still struggle to provide accurate harm reduction information, and sometimes, present severe safety risks to PWUD. This work contributes an evaluation framework for LLMs to deliver harm reduction information to avoid introducing negative health outcomes through the use of LLMs.

cs.CL↗

arXiv2026

The Five Safes as a Privacy Context

The Five Safes is a framework used by national statistical offices (NSO) for assessing and managing the disclosure risk of data sharing. It can be understood as a specialization of a broader concept--contextual integrity--to the situation of statistical dissemination by an NSO. We demonstrate this by mapping the five parameters of contextual integrity onto the five dimensions of the Five Safes. We also discuss how each of these two theories can address weaknesses in the other, thereby strengthening them both.

cs.CR↗

arXiv2026

Can machines think efficiently?

The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions. By adding the constraint of energy, the new test forces us to evaluate intelligence through the lens of efficiency, connecting the abstract problem of thinking to the concrete reality of finite resources. Further, this proposed new test ensures the evaluation of intelligence has a measurable, practical finish line that the original test lacks. This additional constraint compels society to weigh the time savings of using artificial intelligence against its total resource cost.

cs.LG↗

arXiv2026

Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students' Interest

This study examines how age and gender independently shape adolescents' interest in computer science (CS) education. Building on the Person-Object Theory of Interest (POI), we define enthusiasm as a short-term, activating response that combines positive affect, perceived relevance, and intention to re-engage. Because such enthusiasm can shift CS attitudes and engagement intentions even briefly, it offers a useful measure for short outreach activities. We developed a 28-item pre-post questionnaire to assess whether CS interventions raise enthusiasm, then applied it to more than 400 students (244 female, 187 male, aged 10-18) in CS courses. Contrary to the common assumption that early exposure secures lasting interest, we found a marked decline during early adolescence, especially among girls, along with wide variation in interest trajectories across ages. Exploratory factor analysis and ANOVA show that age predicts interest development more strongly than gender, and reveal specific developmental breakpoints. Although older students began with lower baseline attitudes, they showed the largest gains after the intervention, indicating that well-designed short activities can re-engage interest even later in adolescence. These results point to the need for CS education strategies that adapt to developmental stage rather than assuming a single early window matters most. Our validated questionnaire offers a way to measure immediate affective and motivational responses, giving researchers and practitioners a tool to evaluate whether specific interventions succeed in raising enthusiasm.

cs.SE↗

arXiv2026

SafeMath: Safe Solutions for Unsafe Math Word Problems

Recent research points toward LLMs being manipulated through adversarial and seemingly benign inputs, resulting in harmful, biased, or policy-violating outputs. In this paper, we study an underexplored issue concerning harmful and toxic mathematical word problems. We show that math questions, particularly those framed as natural language narratives, can serve as a subtle medium for propagating biased, unethical, or psychologically harmful content, with heightened risks in educational settings involving children. To support a systematic study of this phenomenon, we introduce ToxicGSM, a dataset of 1.9k arithmetic problems in which harmful or sensitive context is embedded while preserving mathematically well-defined reasoning tasks. Using this dataset, we audit the behaviour of existing LLMs and analyse the trade-offs between safety enforcement and mathematical correctness. We further propose SafeMath -- a safety alignment technique that reduces harmful outputs while maintaining, and in some cases improving, mathematical reasoning performance. Our results highlight the importance of disentangling linguistic harm from math reasoning and demonstrate that effective safety alignment need not come at the cost of accuracy.

cs.CL↗

arXiv2026

The Enforcement and Feasibility of Hate Speech Moderation

Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native speakers, representative of a full day on the platform. Five months after posting, 80% of hateful tweets, including violent ones, remained online. Removal was only marginally more likely than for non-hateful tweets, far below scams or adult content, and insensitive to severity and reach. Automated detection could not reliably classify hate but ranked it highly, enabling human triage. Simulating this workflow, current staffing curbed little exposure, yet substantial reductions proved financially feasible, far below applicable regulatory fines. Persistent hate reflects resource allocation, not technical limits.

cs.CY↗

arXiv2026

Qualified Cross-References as a Verification Method: The Normative Environment of the EU AI Act

Legal cross-references are commonly represented as links between instruments or provisions. In a curated legal knowledge base, a link must also identify the legal character of the interaction, its supporting provisions and conditions, and remain consistent from either instrument. This paper presents a provision-level model and a protocol for qualified cross-references, developed through a bilingual corpus of fourteen instruments surrounding Regulation (EU) 2024/1689 (the AI Act). The model distinguishes direct textual reference, bounded presumption of conformity, substantive interaction without textual reference, mediated intersection, and institutional analogy; applicative interaction and definitional overlap are independent dimensions. Its methodological contribution is bidirectional inversion: a relationship documented from act A towards act B is reconstructed from B's perspective against both instruments. This verification tests provisions, qualification, direction, and conditions before deciding how to render the relationship from either side. Applied during construction, the protocol surfaced six incorrect article references, three inaccurate legal qualifications, and one divergence between two published descriptions of the same interaction. A reference to Regulation (EU) 2019/881 illustrates why qualification matters: the AI Act's bounded cybersecurity presumption differs from other product legislation's uses of the same certification framework. The contribution is a map of one regulatory environment and an explicitly specified method for making curated cross-reference knowledge bases inspectable and internally testable. A later, separately scoped corpus audit is discussed among the limitations, without pooling its findings with the original measurements; limited access to historical inputs constrains independent reproduction.

cs.CY↗

arXiv2026

A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI

Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite personnel turnover, model replacement, regulatory change, and shifting organizational incentives. Existing governance frameworks provide important principles but do not by themselves supply a compact mathematical language for evaluating whether an institution can preserve sound judgment over time. This paper develops a design-science framework for institutional legacy: the durable capacity of a decision system to continue producing beneficial, lawful, explainable, and adaptable outcomes after its original designers have stepped away. The framework contributes: (i) a normalized Legacy Score based on a penalized geometric mean of knowledge retention, governance, human oversight, adaptability, feedback learning, and jurisdictional fidelity; (ii) Decision Confidence and Decision Risk models separating evidentiary confidence from consequence; (iii) authority-aware retrieval and calibrated abstention; (iv) Decision Memory for governed organizational learning; (v) Regulatory Change Velocity mapping change exposure to review intervals; and (vi) a federated regulatory knowledge-graph architecture preserving provenance and legal hierarchy. The paper also proposes eight AI Decision Integrity Rules, an evaluation protocol, and a reproducible computational demonstration. The demonstration combines a deterministic stress test with 200 Monte Carlo replications of 10,000 synthetic decisions each, illustrating Legacy Score non-compensation and comparing consequence- and authority-aware routing with a matched-coverage confidence-only baseline. The contribution remains conceptual rather than field-validated; the simulation tests internal behavior, not production performance, and all parameters require context-specific calibration.

cs.CY↗

arXiv2026

Visual Framing for News Stance Detection via Image Generation

Article-level news stance detection aims to identify the perspective of news articles toward social issues. Despite advances in stance detection and its importance for trustworthy media environments, news articles pose distinct challenges because their stances are often implicit, subtly conveyed through journalistic framing, and embedded in long, structurally complex texts. To address these challenges, we introduce VFStance, which leverages visual framing to make implicit stance cues more explicit via image generation. In evaluation experiments, we demonstrate the effectiveness of VFStance over existing methods and the contribution of visual framing to its performance. Finally, a controlled user study (N=200) in a snippet-based news consumption setting further demonstrates that VFStance can make stance signals visually salient and highlights its potential use beyond automated stance detection.

cs.CL↗

arXiv2026

Effective Interventions Against AI-Enhanced Scams

In 2025, scams were responsible for an estimated $442 billion in direct losses globally. In the United States, reported losses increased by nearly 400% between 2020 and 2025. Though AI in scamming is a relatively new phenomenon, its use significantly changes the economics of scams as well as the bottlenecks in scam operations. In this paper I investigate what interventions will remain effective under this new AI-driven scamming regime. I develop a simple model of scam profits to understand how different interventions asymptotically affect scam operations. I find that three levers--reporting rate, centralization of reporting, and report accuracy--multiply in their effect on expected victims per scam channel, reducing revenue per scam channel while increasing costs. Because effects multiply, interventions affecting all three could have a significant effect on the profitability of the scam business model. My analysis suggests that even modest reporting rates against high-value scam infrastructure could have significant impacts on scam profitability.

cs.CR↗

arXiv2026

Causal Evidentiary Governance for High-Risk Machine Learning Systems

Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as the EU AI Act and GDPR. Current fairness governance practices rely on observational fairness metrics, post-hoc explainability, and immutable audit logs, but provide limited support for causal attribution and efficient evidentiary verification. We introduce Causal Evidentiary Governance (CEG), a framework in which regulated institutions commit to a versioned directed acyclic graph (DAG) that partitions causal pathways into allowable and disallowed groups. The Causal Harm Rate measures prediction variation attributable to disallowed causal pathways. Each decision is accompanied by a signed Decision-Evidence Packet (DEP), cryptographically binding the prediction to a digest of the published DAG and path-specific attributions. DEP digests can be appended to a Merkle tree to enable logarithmic-cost inclusion proofs. We validate CEG through a two-layer empirical methodology using demographic summaries from four years of PMA credit supervisory data to construct 10,000 synthetic credit applicants across four strategic DAG counterfactuals. Causal Harm Rate isolates injected causal effects more clearly than demographic parity or equalized odds. Cross-model validation and ablation studies assess robustness. Evaluation on the German Credit dataset shows that harm associated with specific causal pathways can be substantially understated by associational fairness metrics. Finally, a proof-of-concept implementation demonstrates operationally plausible throughput and highlights relevant performance tradeoffs.

cs.CY↗