ALT Deploying conversational assistants at government service desks faces a tension between regulatory certainty (zero tolerance for invented fees), data sovereignty prohibiting commercial clouds, and high rates of underspecified or out-of-jurisdiction citizen queries. We present and evaluate a governed, sovereign Retrieval-Augmented Generation (RAG) architecture deployed on the official portal of the State of Tlaxcala, Mexico (tlaxcaladigital.gob.mx), covering 1,143 procedures and a decoupled directory of 924 offices. The system governs interactions via three pillars: (1) a two-stage tripartite epistemic gate (clarifyₚolicy and a Boltzmann distribution over AnyJev) arbitrating among answering (SPECIFIC), facet-guided clarification (CLARIFY), and abstaining (ABSTAIN); (2) a post-generation deterministic numeric verifier (verifyₙumericguardrail) with SHA-256 cache invalidation ensuring every generated fee or validity figure belongs to the retrieved official record; and (3) an empirical eval

ALT Vehicle theft is a persistent urban problem with large economic and social costs. We analyze 521,098 stolen-vehicle reports filed with the Los Angeles Police Department from 2000 to 2022, at scales from hours to decades and from 100 m grid cells to census tracts. Much of the work lies in making the administrative records fit for analysis: we separate thefts from recoveries and traffic reports filed in the same records, repair time fields, validate the geocoding, and harmonize census data across changing tract boundaries. Theft is strongly and persistently concentrated: 10.8% of 250 m cells hold half of all thefts, and six census tracts are significant rate hotspots in all 23 years after false discovery rate control. Thefts close in space and time are more frequent than expected by chance, but about half of this excess comes from events at identical coordinates. Lower-income tracts have more thefts at equal exposure, monthly temperature shows no association with theft, and thefts peak i

ALT When a problem admits multiple valid solutions, effective tutoring requires building on a student's strategy while introducing alternatives at appropriate points. However, whether LLM tutors can support this flexibility by appropriately responding to students' diverse solution approaches remains largely unexplored. In this work, we introduce MUSE: Multi-solution Understanding and Simulation Environment, a controlled environment for simulating student-tutor conversations on multiple-solution programming problems. Grounded in learning science on responsive instruction and multiple-solution learning, we investigate which solutions LLM tutors prefer, how they respond to students' approaches, and how they transition between solutions. Experiments suggest that LLM tutors exhibit systematic preferences among valid solutions, often favoring brute-force approaches when not providing general advice. More concerningly, tutors often impose these preferences, redirecting students toward their own p

ALT The gap between AI capability deployment and safety assurance is structural and cannot be closed through internal evaluations alone. Analyzing documented harms (2017-2026) across the human lifespan reveals that disparate incidents — from voice assistants endangering children to AI companions implicated in adolescent suicide and automated care denials for the elderly — reflect a single failure: evaluating systems against an implicit adult user model. We introduce five developmental moderators structured into a Lifespan Vulnerability Matrix across six life stages. The matrix reveals a nonmonotonic risk distribution: vulnerability peaks at both ends of the lifespan, governance coverage is asymmetrical, and authority to convert model errors into real-world impact is scrutinized least. We then propose a set of lifespan design principles generalizing existing child-protective instruments, and an institution-agnostic, three-tier external evaluation framework — comprising automated simulation,

ALT Generative AI is putting the law of reverse engineering (RE) under severe strain. Existing IP and technology laws assume that RE is expensive and time-consuming, and that it requires specialized expertise. They generally allow it, but they rely on its difficulty as a filter to separate (permissible) RE from (impermissible) copying and theft. Generative AI is changing this equation by making RE much faster, cheaper, and easier. AI systems can identify hardware components, extract firmware, decompile object code, and clone entire apps. The resulting struggle between technology vendors and reverse engineers will challenge a wide range of laws, including trade secret, copyright, anti-circumvention, computer misuse, and contract. In this article, we describe the ways in which AI is changing RE, map these new practices onto existing laws, and sketch ways that the legal system could respond and their associated trade-offs.

ALT The integration of large language models (LLMs) into clinical decision-making raises unresolved questions regarding diagnostic bias. We investigate whether a documented psychiatric history alters LLM recommendations when the current clinical evidence is identical. Using a counterfactual experiment inspired by Rosenhan's 1973 field study, we factorially varied diagnostic-history condition (no prior diagnosis stated vs. prior schizophrenia diagnosis) and current-evidence condition (voices ceased vs. voices persisted) within a clinical vignette. Across five LLMs (GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.6 Flash, Grok 4.6, and Mistral Large 2512), we generated 20,000 model responses across independent queries. When evidence supported discharge, adding a prior schizophrenia diagnosis increased recommendations for continued hospitalization nearly fivefold, from 4.7% to 22.3% (risk difference, 17.5 percentage points; 95% CI, 16.2-18.8). This shift occurred across all five models (risk-differenc

ALT Artificial intelligence (AI) is changing how data-driven research is produced. As AI tools make information and technical execution more accessible, mentorship must concentrate on judgment, ethics, and the design of end-to-end AI-augmented workflows. Disciplinary experts need mentoring to adopt AI while preserving their expertise and human agency, and AI-native research trainees need mentoring to become polymathic workflow developers. This chapter proposes Everyone's Backyard, an interdisciplinary model in which disciplinary experts, trainees, and community partners mentor one another in both directions and across disciplines through shared research practice in a convergence studio. The model centers on four capacities: statistical thinking, data ethics, collaboration, and AI for social good and society. We illustrate the model with the Spring 2026 aiX Faculty Fellows Program at Columbia University, which brought together 20 faculty fellows and 15 student interns from 14 schools and de

ALT Large language models (LLMs) can generate alternatives, elaborate ideas and supply rationales, overlapping with cognitive work learners are expected to perform. However, human-AI cocreation still lacks a mechanism for determining how AI can scaffold learners' creative decision-making. This study develops a cognitive-pedagogical framework for deriving the behaviour of a pedagogically constrained LLM from an educator-approved specification of each activity in a cocreative process in project-based learning. The creative-process phase supplies the activity's purpose, typical processes and pedagogical conditions; the active section of a creative technique specifies how the group works on its ideas. For each task, an educator approves four fields: a cognitive and metacognitive objective, a pedagogical objective, the task and group artefact as context, and the learner-owned product. The objective identifies the creative operations and decisions about progress, focus, criteria and next steps t

ALT AI representation is emerging in digital democracy, assisting in voter emulation and decision-making. Large language models (LLMs) behave differently depending on the model, the prompt context, and the voter information they receive, representing some groups more accurately than others and potentially making the emulation unfair to certain groups. We investigate demographic biases in LLM-emulated electorates: how accurately LLMs represent individual votes, whether some voter groups are represented more accurately than others, and whether AI alignment can reduce these disparities without sacrificing overall accuracy. Using three U.S. presidential elections, we compare a closed-source and an open-source LLM across voters' demographic features and test AI alignment strategies toward fairer voter representation. Removing gender from the voter profile reduces the largest group error of the models by 22-42%, and removing education reduces it by 64%, whereas removing self-reported race increa

ALT Data protection legislation aims to protect privacy rights by regulating how information systems store and process personal information. While such legislation defines legal obligations, translating regulatory requirements into engineering practice is far from straightforward. As new frameworks such as the EU AI Act emerge, understanding these mechanisms becomes increasingly relevant. To investigate them, we conducted an online survey of 156 U.S. information system developers, examining the role of organizational privacy climate in the association between developers' perceptions of the GDPR and their privacy engineering practices. Using structural equation modeling, we found that organizational privacy climate mediates the relation between developers' perceived GDPR importance and their privacy attitudes, which in turn are associated with their privacy engineering practices. The mediation chain - regulation, climate, attitudes, practices - complements the direct effect of regulation on

ALT Editorial policies increasingly require authors to disclose substantive uses of generative AI, but such requirements commonly focus on whether, where, and how a model was used. Even function-specific disclosure may locate a model in the research or writing process without showing how its outputs entered scholarly judgment, how they were evaluated, or whether contributors can reconstruct the relevant reasoning and defend resulting claims without treating those outputs as sufficient justification. Drawing on research ethics, AI disclosure policy, human-model interaction, and researcher formation, this article argues that disclosure documents model use, whereas responsible use requires claim-level authorial warrant: a contributor's capacity to understand, justify, and defend claims within their contribution. The article distinguishes cognitive delegation, in which model outputs shape substantive choices, interpretations, or justifications, from routine delegation, which does not ordinaril

ALT Large-area accessibility analysis remains difficult to reproduce because multimodal routing, heterogeneous opportunity data, and reusable spatial outputs are rarely integrated in a single transparent workflow. We present MAPPER (Multimodal Accessibility and Place Profiling Engine for Arbitrary Regions), an open-data pipeline for multimodal accessibility analytics. The workflow builds walk, bike, car, and public transport travel-time graphs from OpenStreetMap and GTFS data, harmonizes mapped POI- and proxy layers representing the land-use side of the accessibility relation, and computes continuous accessibility fields using network kernel density estimation. A low-overhead native compute core (contraction-hierarchy routing, C++/OpenMP accessibility accumulation, DAG-style orchestration eliminating redundant computation) is the enabler that makes many graphs, large extents, and a broad layer set jointly tractable on commodity hardware. Outputs are sampled to a uniform H3 grid for downstr

ALT Governments govern AI-generated online harms from commercial platforms (EU DSA, AI Act, UK Online Safety Act), yet the most severe class remains ungoverned: autonomous cyber weapons (ACWs) deployed by nation-states against civilian infrastructure. We address this gap with three contributions: a digital-harm taxonomy (six ACW modes × five harm categories); the Meaningful Human Control (MHC) Model, a governance-compliance function with a closed-form IHL threshold and a proved weight-sensitivity bound; and a platform accountability architecture with treaty language grounded in ARSIWA state-responsibility doctrine, not Rome Statute individual responsibility. Applied to eleven documented state-attributed operations and one hypothetical defensive AI system, every documented case falls in an accountability vacuum; the threshold is target-dependent, and against wholly civilian targets no system studied clears it, including the hypothetical. Three further incidents are correctly excluded on aut

ALT No domestic legislature has ever reviewed a state's decision to authorise an autonomous cyber weapon before it acted. Closing the international attribution gap is necessary but not sufficient for democratic accountability. We formalise the Tripartite Attribution Framework's three attribution levels and add a fourth, non-legal Democratic Accountability level (A_dem): a 72-hour legislative notification requirement. Scoring six democratic oversight bodies against five adoptability dimensions yields a counter-intuitive result: the statutes granting these bodies privileged access to classified material are often the same statutes withholding particular ongoing operations from them, so at most one could lawfully receive such a notification today, and the carve-out Level 4 needs is required in nearly every jurisdiction rather than only the least-equipped. Retrospective analysis of Stuxnet, NotPetya, and Volt Typhoon shows one pattern: legal attribution resolvable, domestic democratic review a

ALT Text-to-image (T2I) systems do not merely render prompts; they circulate defaults for "what X looks like," shaping recognition through pose, camera, costume, props, and setting. While technical audits quantify disparities, they rarely cultivate the literacy needed to argue how images assemble stereotypes and confer authority. This paper translates a three-stage, humanities-informed framework into a set of epistemic games that make this cultural work visible and debatable: Interpret (art-historical close looking with AI outputs), Interrogate (counter-intent prompting as artistic exploration), and Expose (creative misuse through paired prompts with a constant motif and a BiasSlot rule, in which only the bias cue varies). In a pilot with undergraduate fine-arts students, the games shifted discussion from impressionistic reactions to concise, warranted hypotheses grounded in visual evidence, supported by lightweight classroom instruments that enabled reproducibility and structured collabor

ALT Open-ended AI work has no single correct answer to check, so a judge, often another AI, decides each result. Research on these judges asks which AI to use and how to word the rubric. It rarely tests the procedure that the judge follows. Wargames have no correct answer either, and for two centuries the field has designed that procedure: named methods that set how much freedom the referee has, their known failures, and fixes. We map eleven wargaming practices onto AI evaluation failures. We test one, the adjudication method, on 378 player actions from nine games of an AI-refereed exercise. A player states its odds, the adjudicator turns them into a target number, and a 20-sided die decides pass or fail. With rolls fixed, the target came from the rulebook (Rigid), a judge AI adjusting it (Semi-Rigid), a judge AI setting it alone (Free) or a panel of four judge AIs. In evaluation terms these are a fixed scoring rule, a rubric the judge may adjust, holistic grading and a jury. The judge AIs

ALT AI models already inform clinical decisions and draft notes in Australian clinical software, usually without the practitioner being told which model is used. Existing medical benchmarks are written for other jurisdictions. We wrote 142 questions from Australian guidelines and schedules across five domains (PBS authority, MBS billing, Ahpra obligations, triage red flags, immunisation and screening), each with one verifiable answer scored by exact match. Each question ran blind and with the governing clause supplied, which separates failure to recall the current rule from failure to apply it. We tested 28 models between July and October 2026. Blind accuracy ranged from 51.5% to 92.7%, and the highest-scoring models could not be separated. Of 791 failed model-question pairs, 684 (86%) were wrong blind and right with the clause. Confident-error rates ranged from 6.6% to 47.8%. On five immunisation questions where the guideline gives no directive, 82% of answers stated one. On 20 Aboriginal

ALT Technology tools used for professional upskilling change on a timescale of weeks, and written guidance about them ages at the same pace. Large language models have made it inexpensive to draft a tutorial or a course outline, but most deployed generation systems still produce a piece of content once and never check it against what an audience actually does with it. This paper describes an agentic pipeline, organized as a typed workflow graph, that treats instructional content production as a manufacturing process with explicit checkpoints rather than a single generation call. Four node classes structure the graph: deterministic scripts, model steps bound to a fixed rubric, open-ended agent phases, and human approval gates, arranged along a forward production path and closed by a feedback cycle that rewrites generation rules from post level engagement telemetry. Production of content is split into two layers: a demand layer, built from video and community sources, that shapes topic selec

ALT The rapid expansions of artificial intelligence (AI), big data (BD), and information and communication technologies (ICT) are driving substantial growth in electricity, water, and material demands, with data centers projected to consume 6.7–12% of U.S. electricity by 2028 (up from 4.4% in 2023) and AI-related water demand estimated at 4–6 billion m³ annually by 2027. Existing “green AI” and “red AI” discussions have largely emphasized computational efficiency and carbon emissions, providing limited guidance for lifecycle trade-offs, service-versus-use impacts, and broader sustainability externalities beyond energy. To bridge this gap, this paper proposes generalized green AI (GGAI) and generalized red AI (GRAI) and extends the taxonomy to red BD (RBD) and red ICT (RICT) as well as red electronic and electrical engineering (REEE) & red digital technologies (RDT) under a unified Environmental-Economic-Social-Technical/Operational (E/Ec/S/T) framework. Based on representative footprint in

ALT As the use of AI, and consequently its harms, becomes more prevalent, the global discourse around AI has become increasingly risk-aware. With new risks emerging more frequently than ever, a key challenge is how to represent, document, and catalogue AI risks in an effective, interoperable, and queryable manner. Additionally, the European Union (EU) AI Act's requirements for "high-risk" AI systems to manage and document the risks creates the need for open and interoperable tools, methods, and standards that facilitate AI risk management in an ever-changing environment. In this paper, we present ontologies and taxonomies developed to assist with risk management and documentation, in line with the requirements of the EU the AI Act. We developed the AI Risk Ontology (AIRO) to specify AI use cases and their associated risks, complemented by the Vocabulary of AI Risks (VAIR), which populates AIRO with taxonomies of concepts. Key international standards on AI and risk, namely ISO/IEC 23894 on