Computers and Society Papers

ALT Finding, sharing, and reusing data requires common interoperable vocabularies to adequately describe machine-actionable data policies. Semantic Web standards are a proven way to express such information describing data access and reuse. Narrative descriptions get a stable and agreed meaning by formal definitions of terms that have a globally unique identifier. The Data Privacy Vocabulary (DPV) represents a community standard for privacy, data protection, and related topics. DPV uses modular namespaces to define jurisdictions, such as "EU", and to represent concepts from laws within that jurisdiction, such as the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS). In this work, we present the creation of a DPV extension representing the German Health Data Use Act (Gesundheitsdatennutzungsgesetz (GDNG)) that has been set into force in March 2024. The GDNG regulates sharing and using health data for secondary use, such as research purposes and policymaking

ALT Disaster research in high-income settings shows that better-resourced communities see population inflows and faster recovery, while disadvantaged communities suffer prolonged population loss. Whether this pattern holds in rapidly urbanizing cities of the Global South remains unclear. Here we show, using mobile phone data from Pune, India, and Bandung, Indonesia, how neighborhood-level changes in phone presence, a proxy for short-term population movement, relate to flooding and neighborhood deprivation. We combine satellite-derived flood maps with phone records to compare flooded and non-flooded areas over the 30 days around each flood. In both cities, less deprived neighborhoods see phone presence rise after flooding, while more deprived neighborhoods see it fall, consistent with resource-seeking interpretations documented in North American and European disaster research. The cities differed, however, in how deprivation relates to flood exposure: in Pune, more deprived flooded neighbor

ALT Generative AI (GenAI) has spread unevenly across Europe, and it is unclear whether higher use brings higher trust. This paper compares Denmark and Romania, the EU's highest and lowest GenAI users, and asks whether the difference in public and institutional trust between them corresponds to the difference in trust in AI. Drawing on trust, technology-acceptance and digital-divide theory, it is a descriptive, contrasting-case analysis of harmonised data: GenAI usage comes from Eurostat, trust in AI more generally from the KPMG/Melbourne global study and public and institutional trust from the Standard Eurobarometer. In 2025, 48.4% of Danes aged 16-74 had used GenAI in the past three months, against 17.8% of Romanians. Use falls with age and rises with education in both countries, and the relative gaps are far larger in Romania; the patterns remain visible within the published age-by-education categories. Trust in AI does not follow usage: Romanians trust AI as much as Danes or slightly mo

ALT How do broader values shape whether people judge artificial intelligence (AI) as good or bad for humanity? This article analyzes provisional World Values Survey Wave 8 data from 40,960 respondents in 24 country samples using pooled and national prediction models, outcome-independent respondent profiles, and groups of countries. The analysis reveals a cross-country anchor: general optimism about science and technology is the strongest pooled predictor of favorable AI evaluation and recurs among the leading predictors in every national sample. Expert authority, education and information access, selected forms of trust, openness, and demographic position provide nationally distinctive patterns. Eleven value profiles formed without AI attitudes span 49.1% to 62.2% favorable evaluation. Most evaluative variation occurs within these profiles, making them useful contexts for identifying how the same values distinguish favorable from unfavorable evaluators in different social configurations. I

ALT Scaling assessment in higher education faces two intertwined bottlenecks: the cognitive burden of authoring assessments and the inability to semantically evaluate open-ended responses, which together create a persistent feedback gap that hinders student learning outcomes. To address these challenges, we present Edhivo, an AI-native pedagogical platform that addresses both bottlenecks through a vertically integrated system for content delivery, assessment, and reporting. Edhivo employs a Dual-LLM pipeline: one model generates context-aware assessments for both multiple-choice and open-ended questions, from instructor-provided materials, while a second model performs multi-criteria, rubric-based evaluation of student responses, enabling near-instant qualitative feedback at multiple hierarchical levels. All AI interactions are source-grounded to reduce hallucination risk, and a Human-in-the-Loop review process preserves instructor authority. We demonstrate Edhivo's efficacy through a pilo

ALT Prices formed by Web users on Polymarket, a blockchain-based prediction market, are now cited in the news and in a U.S. Army intelligence bulletin as early warnings of geopolitical events. However, it remains unclear whether these prices can serve as such warnings. This cannot be judged from accuracy, because most markets resolve No and accuracy can remain high even when many of the events that did happen are missed. In this paper, we study this question from two aspects, using all 1.24 billion on-chain trades in 2.37 million Polymarket markets through September 2026. First, from 3,686 markets on geopolitical conflicts, we derive 208 unscheduled events that did happen and measure how far in advance prices move before the first Web report of each event. Second, we separate the information in prices about whether an event happens from the information about when it happens. To do so, we group 1,551 contracts into 338 deadline ladders and read each ladder as one forecast of the event date.

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