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Computer Science Computers and Society

cs.CY Watch Computers and Society

Saturday 26 September

Computers and Society · Cross-listed from cs.CR

Reading Is Not Leaking: Local, Auditable Measurement and Reduction of Inference Exposure from Public Footprints

Mahmudul Faisal Al Ameen

Anyone with a public footprint leaks facts that were never stated, and language models make the inference cheap. We present a framework for measuring and reducing this inference exposure that runs on the owner's own CPU with no language model at analysis time, instantiated on organisations and on individuals. It starts from a measurement result: scoring an inference system against the target's private truth conflates how well the system reads the record with how much the record leaks. On a 128-question instrument over sixteen synthetic firms, almost half of the questions are never answered correctly by any of six readers, four of them language models, and a majority-class guess accounts for most of every reader's score. We therefore separate reading accuracy from leakage rate and introduce an injection protocol that creates cells with known support. Our analyser combines rules, statistical solvers and a 106M-parameter encoder trained from scratch that marks verbatim evidence and never generates text; every answer carries a graded certificate whose recorded proof replays. Its certified answers are correct in 93% of resolved cases, against 49-73% for the language models' quote-backed answers, whose citations are produced alongside the answer rather than deriving it; with plain-prose articles in the record, 70% of its evidence-bearing answers rest on evidence that establishes them, against 18-56% for the models. A constrained defence that rewrites each fact's carrier as a true but coarser statement hides every single-carrier fact from four language-model adversaries at 40% lower edit cost than deletion. On sixteen synthetic people the guessing term is larger still, and a decoy planner with no language model halves the correct answers of the estimator it targets without transferring to a second.
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Computers and Society · Cross-listed from cs.AI

Artificial intelligences and human scientists exhibit complementary strengths in theory building

Ke Li, Spyros I. Zoumpoulis, Phanish Puranam et al. (+80 more)

We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as theory formulation, predictions of novel empirical results, and theory revision in response to new evidence. The research domain was academic discourse regarding gender and race inequality. Our findings, comparing 25 LLMs with 13 senior researchers and 60 doctoral scholars, reveal that the AIs outperformed most humans individually on most of the present tasks, while human theories were more diverse and exhibited greater gains in predictive accuracy from aggregation. AI-generated theories were more extensively elaborated, involving additional theoretical paths and latent variables, and were rated as higher quality than human theories by independent raters blinded to source. However, this theoretical complexity was in part ornamental, in that it was not associated with more accurate predictions about empirical patterns in data; in contrast, human scientists achieved greater predictive efficiency with simpler theories. The AIs were significantly more likely than human scientists to revise their theories to incorporate new evidence; human scientists updated their beliefs in a selective way that is sensitive to prior prediction errors. We speculate that the superior processing capacity of artificial intelligences makes them especially well-suited to tasks requiring grappling with complexity, but that the greater diversity of human ideas is essential to wise crowds and collective creativity.
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All authors Ke Li, Spyros I. Zoumpoulis, Phanish Puranam, Philip Parker, Matthew Eshbaugh-Soha, Izzy Gainsburg, Michael Gilead, Igor Grossmann, Britt Hadar, Yoel Inbar, Almog Simchon, Robb Willer, Rui Ai, Ruicheng Ao, Gavin J. Bala, Matthew Bidwell, Shuang Cai, Kai Chang, Skyler Y. Chen, Cory J. Clark, Irmak Dai, Abhinandan Dalal, Connor Douglas, Alexis Du, Zhehang Du, Leyun Feng, Isabel Fernandez-Mateo, Linnea Gandhi, Cyrille Grumbach, Anmol Gupta, Vansh Gupta, Maria Hademer, Jay H. Hardy III, Chen Kai Huang, Jacob Xiangyu Jin, Ufuk Keskin, Na Hyun Kim, Mert Kobaş, Byounghoon Koh, Gabrielle Lamont-Dobbin, Gregory Lanzalotto, Sun Young Lee, Dingzhe Leng, Chenjun Li, Weiyuan Li, Zeyuan Li, Zhongyuan Liang, Ning Liu, Peihong Liu, Yuhan Liu, Jiuyao Lu, Wanteng Ma, Nicolas Martinet, Natnael Mulat, Christina A. Nguyen, Khai Nguyen, Quang Minh Nguyen, Naja Pape, Chanwoo Park, Stefanos Poulidis, Jeffrey Sanchez-Burks, Michael Schaerer, Isabelle Solal, Yanbo Song, Junghyo Sun, Qingyao Sun, Rui Sun, Roderick Swaab, Kevin Tan, Dequn Teng, Michelle A. Vaccaro, Robin Vigerbaeck, Xiaomeng Wang, Randol H. Yao, Duygu Yilmaz, Shun Yiu, Ecem Yucesoy, Allen Zang, Ruijia Zhang, Xilan Zhang, Yichi Zhang, Zhanhao Zhang, Eric Luis Uhlmann

Computers and Society · Cross-listed from cs.CL

Language as an Independent Information Layer: A Conceptual Model of Communication, Cognition and Decision-Making

Anastasiia Alifanova and Elena Benderskaya

Based on an analysis of the role of language in thought and communication, this article proposes a new concept for designing corporate knowledge bases. The concept integrates the probabilistic vector space of a corporate vocabulary, reflect-ing industry specifics, subject focus, terminology, and culture, with traditional ontological modeling. This combination enables the efficient extraction of knowledge from accumulated corporate documents while strictly accounting for specific business processes. Consequently, this concept bridges statistical and semantic (cause-and-effect) methodologies. Furthermore, analyzing the projec-tions of probabilistic spaces and causal relationships can help identify bottlenecks in business logic. As a dynamic system, language functions as a separate, inde-pendent layer within the overall information architecture. Introducing a dynamic component into the probabilistic space of word distribution allows it to be mod-eled as a multidimensional solution space for various problem formulations. In this context, input data defining the problem conditions serve as control parame-ters for dynamic transformations.
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Computers and Society · Cross-listed from cs.LG

Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation

Takayuki Mizuno, Shouji Fujimoto, Mikito Hiruki et al. (+1 more)

Geospatial simulation of infrastructure interventions requires mobility generators that respond directly to edited maps, yet many data-driven generators do not expose the map as an editable condition. We formulate this task as map-conditioned autoregressive generation of human mobility: a road raster conditions a decoder that emits nominal 31.25 m mesh-cell tokens at one-minute intervals. The mesh-local vocabulary supports held-out and locally edited maps without retraining or vocabulary changes. We instantiate a ResNet-50 visual-prefix configuration and a Vision Transformer (ViT) cross-attention configuration, trained from scratch on 87,400 smartphone-derived trajectories from 874 meshes in Ishikawa Prefecture, Japan; 219 meshes are held out. We evaluate map sensitivity by comparing correct-map and within-split shuffled-map generations with held-out real trajectories. On the 110-mesh test split, for the ResNet-50 configuration, correct-map generations are closer than shuffled-map generations on 60% of meshes under Hausdorff-based energy distance (p = 0.021), while DTW is directional but inconclusive (57%, p = 0.074); correlation with real density is 0.38 with the correct map versus 0.01 with shuffled maps. The ViT configuration shows weaker trajectory-level sensitivity and smaller density gains. An illustrative bridge-removal edit changes generated continuations without retraining. Together, these results support the feasibility of editable-map human-mobility simulation.
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All authors Takayuki Mizuno, Shouji Fujimoto, Mikito Hiruki, Atushi Ishikawa

Computers and Society · Cross-listed from cs.CL

Supporting and Performing Culture from the Inside

Lea Frermann and Steven Bird

Anthropology and related disciplines which study culture have often found it useful to consider their epistemological and methodological approaches in terms of emic versus etic. This is the distinction between the insider perspective, how the world is viewed by a member of the culture, versus the outsider perspective, the scientific cataloguing of cultural knowledge and practice. We adopt this framework to systematically analyse assumptions in recent research on culture in NLP along the full pipeline of task selection, data collection, system design and evaluation. We draw attention to the `culture of NLP' which shapes the research focus and approaches of our field, and suggest pathways towards a more culturally attuned AI.
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Friday 25 September

Computers and Society

CALLIOPE: A Source-Grounded Oral Assessment System and Synthetic Readiness Evaluation

Nizam Kadir

Oral assessment with generative AI requires more than a conversational interface: educators must connect a spoken response to its source material, scoring criteria, model outputs and subsequent human judgement. This technical report presents CALLIOPE, a source-grounded oral assessment system integrating versioned instructional material, learner-turn recording and transcription, adaptive questioning, two-provider rubric scoring, educator review and exportable evidence. We examine implementation and retained synthetic verification records from 25 September 2026. Three spoken fixtures and a silence control were exercised across two release runs. In the final release rehearsal, the spoken fixtures received aggregate AI scores of 100, 62 and 8 out of 100; two elicited provider-disagreement flags. All eight retrieved audio files across the two runs were byte-identical to their inputs. These observations establish operation of specific exercised paths, not scoring validity or learning gains. A later zero-traffic candidate added version-bound consent checks, insert-only first-pass rating receipts and separately authorised coded exports, supported by local regression tests but not a new full live research workflow evaluation. We distinguish deployed functionality, candidate safeguards and remaining recovery, concurrency and study-operation requirements. The contribution is an inspectable response-to-review workflow and a release-specific account of what its engineering evidence does, and does not, establish. No human-participant outcomes are reported. OpenAI Codex assisted with technical verification, evidence synthesis and manuscript preparation.
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Computers and Society

TeamLens in Critsly: A Consent-Based Team-Composition Interface and Synthetic Readiness Evaluation for Design Collaboration

Nizam Kadir

Discussing working preferences may support reflection within a design team, but a personality label should not become a performance prediction or a condition of participation. This technical report presents TeamLens, an optional Critsly interface for voluntarily sharing a self-reported MBTI type with a particular board. It separates account activation from disclosure, displays descriptive composition counts, and provides distinct controls for disabling visibility, withdrawing one report and deleting all of one's reports. The evaluation combines released-source inspection, independently specified synthetic aggregation cases, access and lifecycle checks, browser component tests, a local database microbenchmark and deployment records. All 65,536 binary eligibility subsets of sixteen fixed, distinct type reports matched an independent oracle; 128 seeded multiplicity fixtures also matched, after 4,259 synthetic share calls. The isolated HTTP suite passed 183 assertions. In 180 sequential in-memory SQLite read trials, median service-call time increased from 0.064 ms with no profile rows to 200.932 ms with 1,024 rows; instrumented SQL operations followed 11+3n. These findings concern exercised software behaviour and a bounded workload. They do not establish human usability, psychometric validity, learning gains, team-performance effects or production capacity. The contribution is an implemented disclosure-to-aggregation workflow and an auditable technical account of its correctness boundaries, privacy limitations and scaling cost. OpenAI Codex assisted with implementation, evaluation and manuscript preparation; the paper discloses this use and its limits.
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Computers and Society · Cross-listed from q-fin.GN

Multi-Jurisdictional Legal Identity Assurance for Capability Gating: A Design-Science Proposal for Tiered, Reusable Identity Assurance of Natural, Juridical, and Machine Entities

Walter Kurz

Identity assurance is the cost a digital system pays for dishonesty and uncertainty: it exists to make acts attributable when not everyone can be trusted at their word. A common way to pay that cost is flat maximum verification, asking each participant to meet a single high level of identification at entry, before any capability is exercised. Paid on everyone, it over-collects, excludes participants who cannot meet a bar they never needed to clear, taxes every interaction with the cost of the rarest high-risk case, and binds the strength of identification to the activity it unlocks. Existing frameworks compound this by fixing a small number of per-credential levels inside a single legal space and binding each verification to the institution that performed it, leaving cross-border reuse and the tension between data erasure and evidentiary retention unaddressed. This paper develops, as a design-science proposal, a tiered and reusable model of identity assurance for natural, juridical, and machine entities across jurisdictions. Reading the problem through systems theory, where a system changes only when an entity acts, the model holds the assurance state apart from the capability gate that consumes it, so that identity demand follows the act and the weight of its consequences rather than mere presence: a participant may take part with minimal disclosure and supply more only as an act requires. It comprises a typed entity taxonomy, a two-axis coordinate of disclosed assertion scope and source of information, jurisdiction as a time-indexed attribute of the entity, and reliance recorded as bitemporal, liability-allocated, point-in-time snapshots. Requirements are derived from anti-money-laundering, electronic-identity, and data-protection law, and the proposal is evaluated against flat maximum verification, per-credential level-of-assurance designs, and institutional reusable-KYC reliance.
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2610.00287 · 25 Sep · 29 pages, 3 figures, 9 tables. Written to solve the AML/KYC problem in financial services: proportional customer due diligence, beneficial ownership and reusable third-party reliance under EU AMLR, AMLD4 and FATF. Covers natural persons, legal entities and machine actors from bots to AI agents; the gates extend beyond finance, e.g. to protecting minors. Published in Swissi AI Journal, CC BY 4.0

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Computers and Society · Cross-listed from cs.LG

Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility

Samet Hicsonmez, Nermin Samet and Renaud Marlet

In the field of Machine Learning, many papers contain empirical results supporting claimed statements or illustrating the performance of a proposed method. However, most practitioners know that (1) results are generally hard to reproduce, and increasingly so, (2) code is not often available to do so, and (3) it hinders the development of research. In this position paper, we analyze and quantify these issues, and make concrete proposals to improve result checkability, if not reproducibility. Code and supporting materials are available at https://github.com/giddyyupp/position-enforce-verifiability.
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Computers and Society

Period Segmentation in Transition Network Analysis: A Topological Data Analysis Approach

Hitoshi Inoue and Koichi Yasutake

Temporal dynamics in learning behavior can be revealed through period segmentation in Transition Network Analysis (TNA). Cristea et al. demonstrated that segmenting courses into halves and quarters reveals how learning strategies evolve and relate to academic performance. Building on this approach, we investigate whether Topological Data Analysis (TDA), specifically connected components ($\beta_0$) change points from Zigzag Persistent Homology, can provide data-driven period boundaries that identify intervention windows. Analyzing 22 courses from the Open University Learning Analytics Dataset, we find that $\beta_0$-based segmentation captures greater between-period variation than time-based segmentation (median variance ratio (VR) = 4.10$\times$; 21/22 courses show VR $>$ 1.1). Permutation tests confirmed significance ($p < 0.05$) in 18% of individual courses, with 73% showing positive improvement over random breakpoints. Only one course showed better performance with time-based segmentation. However, analysis of academic outcomes reveals a key insight: final-period behavior shows lower correlation with outcomes in $\beta_0$-based segmentation than in time-based segmentation, reflecting a marked behavioral collapse where engaged learners rapidly disengage. We interpret this as evidence that final-period behavior reflects rather than causes outcomes: students who will pass maintain engagement, while those who will fail disengage. This reframes the value of $\beta_0$: rather than improving prediction, change points identify intervention windows. These are periods where behavioral structure shifts and targeted support may be most effective.
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Computers and Society

Who Governs Data in the AI Era? A Computational Analysis of the U.S. Privacy Workforce in Job Postings

Ramazan Yener, Muhammad Hassan and Masooda Bashir

Privacy protection now spans legal, technical, and managerial duties, and demand for privacy professionals is growing across sectors. However, little is known about how employers define these roles. We analyze 1,143 U.S. privacy job postings from LinkedIn and Indeed. We examine job titles, salaries, competencies, certifications, education, experience, regulatory references, and AI-related language by using rule-based text mining. We also apply Topic Modeling (BERTopic) to the same postings and identify 18 latent themes which we grouped them into four categories. Our findings show that privacy roles are hybrid and they combine legal knowledge, technical skills, and interpersonal competence. Artificial intelligence appears in more than half of postings, with AI language spread across compliance, legal, governance and security themes. Our research indicates that AI governance responsibilities are often embedded within existing privacy roles, contributing to the rise of hybrid positions alongside dedicated AI governance roles.
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Computers and Society · Cross-listed from cs.AI

A Benchmark for LLM's Understanding of Middle School and High School Science Topics

Noah L. Schroeder, Yessy Eka Ambarwati, Yuji Zhang et al. (+1 more)

Large language models (LLMs) are increasingly integrated into educational settings, yet educators lack robust, standards-aligned tools to evaluate their effectiveness in K-12 science contexts. Existing benchmarks predominantly assess general language or advanced scientific reasoning, leaving a critical gap in understanding LLMs' performance on content directly relevant to secondary science curricula. To address this gap, we developed a comprehensive NGSS-aligned benchmark for both middle and high school science using a rigorous synthetic data pipeline, multi-judge validation, and item-level psychometric analysis. Nine open-weight LLMs were systematically evaluated using this benchmark, indicating that several smaller, locally deployable models achieved high accuracy across diverse science domains and question types. Our findings indicate that model size did not consistently predict performance, emphasizing the importance of intentional model selection for educational deployment. We then incorporated a human reviewer into the loop, reviewing the items generated by the LLMs for alignment with NGSS standards. The human review indicated that synthetically generated items were not in perfect alignment with the NGSS standards, indicating the benefits of human-in-the-loop item development, the need to explore the intersection of content and pedagogical knowledge, and the need to extend benchmarks to evaluate LLMs' capacity for interactive, evidence-based feedback in educational scenarios.
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All authors Noah L. Schroeder, Yessy Eka Ambarwati, Yuji Zhang, ChengXiang Zhai

Computers and Society

Anatomy of a Spreadsheet Failure, Analysing the EuSpRIG Horror Story Corpus

Simon Thorne and Angela Collins

Spreadsheet disasters have been documented for more than thirty years, but they have not gone away. This paper examines the EuSpRIG horror stories archive as evidence of how ordinary spreadsheets fail and the consequences that follow. After cleaning and deduplication, 121 incidents from 1995 to 2026 were classified into twelve failure categories and analysed by mechanism, context and consequence. Formula errors, data-entry slips and data-handling mistakes account for 71 cases and recur across the whole period, from Fidelity's missing minus sign in 1995 to a mistyped date that cost Norway's sovereign wealth fund about $92 million in 2024. A second pattern concerns information present in a workbook but not visible to the recipient: hidden rows, hidden sheets, pivot-table caches, embedded objects and retained source layers can cause harm even when no calculation is wrong. The clearest rise in reported cases is in this hidden or embedded data category, whose most serious example is the 2022 Ministry of Defence spreadsheet that exposed details of roughly 18,700 Afghan applicants through hidden rows. The paper argues that spreadsheet risk is no longer only a problem of wrong numbers. It also involves visibility, disclosure, governance and trust. Existing taxonomies remain useful for formula, entry and handling errors, but do not fully capture the public harm caused by spreadsheets used as uncontrolled operational infrastructure. The same mistakes have persisted for three decades, what has changed is the authority organisations give to the spreadsheets that contain them.
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Computers and Society

Agentic Economies for Autonomous Scientific Discovery

Nenad Tomasev, Matija Franklin, Atoosa Kasirzadeh et al. (+4 more)

Recent advances in agentic Artificial Intelligence (AI) systems have marked a shift in AI for Science: moving away from the use of individual AI systems for narrow task execution, toward multi-agent systems capable of orchestrating complex, end-to-end research workflows and performing (semi-)autonomous scientific discovery. The development of multi-agent AI-for-science systems has primarily focused on improving the cognitive capabilities of AI systems, specifically by making advanced reasoning and hypothesis generation more reliable. However, focusing only on cognitive capability improvement could ignore appropriate management of resources, a key bottleneck in scientific discovery. Testing and validating scientific hypotheses and experiments is, physically and economically, resource-intensive and resources are limited. This means that to make significant advancements in autonomous scientific discovery, such as improving human-AI co-scientist complementarity or reaching a truly closed-loop automated process, we must pair ongoing improvements in reasoning capabilities with robust resource management. In this paper, we outline an infrastructure for AI resource management by developing the necessary foundations of scientific agent economies, markets, and institutions. The aim of this infrastructure is to empower AI agents and human scientists to effectively (i) collaborate and establish research priorities, (ii) assign credit, (iii) track accountability and liability, and (iv) safeguard against malicious use and information security risks. Finally, we engage with the macro-level societal implications. of (semi-)autonomous scientific discovery to inform the development of governance policies ensuring an equitable distribution of AI-driven discoveries and derivative future technologies.
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All authors Nenad Tomasev, Matija Franklin, Atoosa Kasirzadeh, Vivek Natarajan, Alan Karthikesalingam, Sebastien Krier, Simon Osindero

Computers and Society · Cross-listed from cs.CR

Verifiable Randomness for Blockchain-Based Lottery Systems

Gonçalo Ferreira, André Zúquete and Paulo Bartolomeu

As lotteries and other high-stakes decentralized applications increasingly depend on unpredictable randomness for their operations, the lack of a secure and transparent on-chain random number generator that is verifiable by all participants remains a critical open problem. Various approaches to blockchain-based random number generation have emerged over the years, each with their own strengths and limitations, and have consistently been superseded as blockchain technology evolved. This paper surveys existing approaches to on-chain randomness and proposes a new platform that builds upon the well-known commit-and-reveal scheme while directly addressing its principal vulnerability, the last revealer attack, in which the final participant can withhold their reveal in order to bias or abort the output upon seeing an unfavorable result. We further compare this solution with prior approaches and evaluate its entropy properties. The proposed architecture combines a web-based front-end with a Solidity smart contract deployed on the Polygon 2.0 blockchain. Implemented and tested on the Amoy testnet, the prototype is low-cost and simple to deploy, providing a practical, accessible proof-of-concept for verifiable on-chain randomness.
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Computers and Society · Cross-listed from cs.SI

Who Belongs Together? Topical and Social Structure in Bluesky Starter Packs

Sima Adleyba and Onur Varol

Bluesky starter packs are human curated collections of accounts and feeds aimed at helping users discover new communities, especially during onboarding. Prior research has examined their effects on platform growth and account visibility. However, whether these packs capture meaningful topical and social structure remains unanswered. We study 2718 active starter packs containing approximately 144K accounts, combining pack metadata, follow graphs, user post histories and inferred demographic attributes to answer this question. We assign starter packs to 17 topical categories, and examine semantic coherence, curator–member resemblance, shared audiences, and follow reciprocity. We found that pack members are closely aligned with both their fellow pack members and their pack's description compared to accounts included in other packs of the same topic, indicating topical specificity beyond the broader topical categories. We also identify semantic resemblance between curators and their pack members, while demographic resemblance varies by attribute and topic. Network analyses show higher follow reciprocity among members of the same pack and between members and curators than between a pack member and either another member from the same broader topic or a nonmember. Members also share substantial portions of their follower networks with fellow pack members. Despite this coherence, a typical member's follower follows only a small fraction of other members in the pack, indicating limited audience coverage. These results suggest that starter packs capture coherent topical and social groups while leaving opportunities for further account discovery. More broadly, results show human-curation can be useful to capture relevant communities and connections, and platforms could integrate human-curation into recommendation systems as a complement to algorithmic systems to expand opportunities for discovery.
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Computers and Society

Research with AI Agents: How Agentic Systems Are Changing Scientific Work

Johannes Lotz and Markus Wenzel

Background. Agentic AI systems independently decompose tasks such as literature search, data analysis, and programming into subtasks, search the web, access databases, and execute code. This allows them to perform digital research tasks at high speed. Objectives. Under what conditions does the use of agentic systems produce reliable efficiency gains, and which tasks remain with researchers? Materials and methods. Summary of current studies on literature searches, data analysis, software development, and clinical decision support. Results. For digital activities, work shifts from execution to steering and review. Efficiency gains are greatest when expected behavior can be formalized in advance and tested automatically. In complex agentic systems, recorded sequences of reasoning steps and tool calls can quickly become too extensive for human review. Furthermore, explanations generated by the model do not reliably reflect how an output was produced. One possible step toward more reliable systems is the validation of individual components. The limited reviewability extends beyond research itself; the review of scientific articles and grant proposals is also reaching capacity limits. In pathology, curated and annotated data, researchers' own analytical skills, and institutional exchange of experience are becoming increasingly important. Conclusions. Researchers remain responsible for their results. They must determine what to delegate and how to review the results. The importance of a research question to patients, the field, and society cannot be fully assessed using formalized criteria and remains a matter of expert judgment. Agentic systems can free up time for this.
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Computers and Society

How can AI accelerate the green transition?

Jacques Sainte-Marie, Marie-Fleur Simmet and Fanny Terrier

In his essay The Gift, published in 1925, the sociologist Marcel Mauss emphasised the 'total' scope of the phenomenon of gift-and counter-gift-giving, noting that it 'expresses, simultaneously and all at once, all manner of institutions: religious, legal and moral (...)\,; economic (...)\,; not to mention the aesthetic phenomena to which these facts give rise (...).” The emergence of artificial intelligence (AI), which is suddenly and profoundly disrupting individual and collective lives, bears a resemblance to the ”total social fact” described a hundred years ago. AI refers to technologies designed to simulate human intelligence, namely an ”automated system that (...) deduces, from received inputs, how to generate output results such as predictions, content, recommendations or decisions that may influence physical or virtual environments.” 4 (OECD). Having become central to human relations, AI relies on physical and globalised infrastructure and therefore, also has an impact on the environment. This paper analyses some of these effects and how they are perceived by society. This text offers an exploration of the links between AI and the ecological transition, seeking to identify new opportunities and constraints. To this end, it will identify the barriers to the ecological transition (1) and the environmental impacts attributable to AI (2). It will then go on to describe how to live in a constrained world (3) while AI accelerates scientific discovery (4) and profoundly transforms society (5).
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Computers and Society

Incipit: Axiom-Grounded Scaffolding for Human-AI Literary Creation

Qiang Liu and Chunyi Zhao

Large language models can produce fluent prose from short prompts, but direct prompt-to-text interaction gives writers limited access to the assumptions that shape a long narrative. We present Incipit, an implemented research prototype that introduces an explicit planning layer between a writer's intent and generated prose. This layer is grounded in literary axioms, defined as curated and reusable propositions about human experience and narrative craft. The prototype connects a knowledge base of 1455 axioms and 472 typed relationships with a five-round direction dialogue, a retrieval-and-selection pipeline, and a three-level blueprint covering creative premises, story beats and character arcs, and chapter outlines. Writers can inspect and edit these structures before using them as context for scene generation. Additional modules support real-event abstraction and five-dimensional diagnostic feedback. We describe the system design rationale, data flow, implementation boundaries, and a worked design example. As no controlled user study or independently rated output study has yet been completed, we do not claim that the system improves literary quality. Instead, we outline a future preregistered evaluation designed to distinguish the contribution of axiom grounding from that of hierarchical planning. The paper contributes a concrete architecture for using literary knowledge as an inspectable coordination object in human-AI writing.
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Computers and Society

Large language models underestimate and partly misrepresent cultural variation in everyday norms

Kimmo Eriksson, Irina Vartanova and Pontus Strimling

A key aspect of culture is a society's norms about everyday behavior. How accurately do large language models (LLMs) represent cultural differences in such norms? To answer this question we used the recent Global Study of Everyday Norms (GSEN), which collected ratings of 150 scenarios in 90 societies, as the human benchmark. We prompted GPT-5 to estimate each society's average rating for every scenario, and later repeated the benchmark in three other LLMs: GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. Compared to GSEN estimates, all four LLMs misrepresented cultural variation in two ways. First, they greatly underestimated its magnitude, estimating differences between societies to be, on average, less than half their measured size. Second, for many scenarios the LLMs poorly identified the pattern of variation, that is, which societies judged the behavior less acceptable and which societies judged it more acceptable. The pattern of variation was identified better for scenarios that elicit concerns about vulgarity, especially scenarios involving kissing and flirting. We also found that norms in more developed societies tended to be estimated somewhat more accurately, and that prompting in local survey languages rather than English produced only a modest improvement in accuracy. Local-language prompting also reduced, but did not remove, the underestimation of between-society differences. Cultural differences in everyday norms are only weakly and unevenly represented by LLMs.
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Computers and Society

Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

Farnaz Farid, Tashfia Towkee, Sania Nasreen et al. (+1 more)

As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisible. While energy and carbon impacts are widely recognized, the substantial freshwater demands of data center cooling and electricity generation receive little attention. To address this gap, we introduce SustainAI, a water-aware, closed-loop framework incorporating environmental accountability into AI deployment. SustainAI integrates real-time water metering, a hallucination-aware penalty model, and a water-aware routing algorithm that accounts for regional water stress. Evaluated via Small Language Models (SLMs) extracting health misinformation, results reveal an 11-fold variation in water footprint across geographically distributed data centers (0.0477 mL to 0.5360 mL per inference). Across 1,335 inference runs, the system consumed approximately 399 mL of water but produced only 240 correct outputs, demonstrating that substantial resources are spent on inaccurate responses. Crucially, SustainAI extends beyond technical optimization through a Care by Design lens, framing AI sustainability around relational ethics, regional equity, and ecological stewardship. By combining water monitoring, adaptive accountability, and Care by Design principles, SustainAI provides a practical foundation for integrating ethical care and environmental responsibility into AI infrastructure design and lifecycle management.
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All authors Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad

Thursday 24 September

Computers and Society · Cross-listed from cs.LG

HydroSphere: A Framework for Governed, Self-Healing Wastewater Infrastructure

Prabu, Fancy C, Suresh A et al. (+1 more)

Rapid industrialization and urban growth are increasing pressure on water quality and wastewater treatment systems, while conventional treatment plants often rely on static monitoring and control strategies that cannot easily adapt to changing pollutant conditions. This paper presents HydroSphere, a governed, data-driven framework for real-time water quality monitoring, forecasting, treatment optimization, and fault recovery. HydroSphere is evaluated using 2.82 million water-quality measurements collected between 1940 and 2023. The framework integrates three main components. First, a hybrid TCN-LSTM model performs multi-step forecasting across seven water-quality parameters, achieving an RMSE of 0.1417, MAE of 0.1047, and R2 of 0.3596. Second, the Adaptive Dosage Optimization Module uses PPO reinforcement learning to adjust chemical dosing, achieving a mean step reward of 1.059 compared with 1.017 for a fixed-dose baseline. The results also show that unconstrained reward optimization can lead to excessive dosing, demonstrating the need for explicit operational safeguards. Third, the SHADE anomaly detection module uses a deep autoencoder to identify sensor and process anomalies, achieving an F1 score of 0.651 under controlled fault injection. HydroSphere combines these capabilities with tiered governance, deterministic safety bounds, and human oversight to support safer and more adaptive water infrastructure. The framework provides a scalable foundation for intelligent wastewater management and supports the objectives of UN Sustainable Development Goals 6 and 13.
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All authors Prabu, Fancy C, Suresh A, Srini Ramaswamy

Computers and Society · Cross-listed from cs.HC

Epstein Files Engine: Agentic Search for Investigative Journalism

Duy K. Nguyen, Teresa Mondría Terol, Dylan Freedman et al. (+1 more)

On Jan. 30, 2026, the U.S. Department of Justice released a mixed-media collection concerning Jeffrey Epstein, including about three million pages of PDFs. We describe the Epstein Files Engine, an A.I. agent The New York Times deployed to investigate the files. The Engine translated reporter questions into Google BigQuery SQL queries across three corpora: Epstein-related releases, the Times's archive and external, Epstein-related news headlines. It used an LLM to plan queries and returned citation-rich answers a reporter could verify and trust. More than 100 journalists used the Engine, and it contributed to at least 20 published stories. We report how reporters queried it and describe Diff, our text-and-visual duplicate matching method that amplified novelty signals and allowed the Engine to surface genuinely new information. We argue that newsroom agents serve newsrooms best not as autonomous writers, but as interfaces to source material and institutional knowledge.
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All authors Duy K. Nguyen, Teresa Mondría Terol, Dylan Freedman, Zach Seward

Computers and Society · Cross-listed from cs.LG

Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation

Zhaoyang Cao, Miriam Metzger and Reza Zafarani

Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed computational framework that translates cross-disciplinary theories of fake news into measurable features for automated detection and explanation through statistical techniques and large language models. To that end, we conduct a structured cross-disciplinary review of theories from social sciences, psychology, economics, among other disciplines that reveal how fake news persuades and spreads, thereby establishing a broad theoretical foundation for computational modeling. Experiments on benchmark datasets show that theory-derived features are predictive and provide interpretable, theory-referenced diagnostic signals. Multi-feature models generally outperform individual features, although gains among the strongest small feature combinations are modest. Our work highlights the value of interdisciplinary perspectives in building robust and interpretable fake news detection systems, advancing the foundation for human-centered approaches in combating disinformation.
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Computers and Society · Cross-listed from cs.GT

Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring

Itai Ashlagi, Ramesh Johari, Jon Kleinberg et al. (+1 more)

AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs. But by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. We study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience. Among the four applicant types defined by experience and compatibility for the job, inexperienced-compatible applicants are the most exposed: they lack observable experience and lose the individualized information that could distinguish them from other inexperienced candidates. When screening is costly, these changes can also generate inefficient screening failures in which firms screen no applicants or screen only experienced applicants. We then show that multistage hiring can arise as an endogenous firm response: a relatively inexpensive intermediate assessment allows firms to acquire new evidence of fit before costly full screening. This can restore screening opportunities that disappear under one-stage hiring and give inexperienced-compatible applicants a path to screening. Our results show how AI can shift the central friction in hiring from submitting applications to obtaining credible evaluation, creating entry barriers for high-fit workers without prior experience. Multistage hiring can endogenously arise in response, restoring evaluation opportunities that would otherwise disappear and helping preserve market functioning.
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All authors Itai Ashlagi, Ramesh Johari, Jon Kleinberg, Anushka Murthy

Computers and Society · Cross-listed from cs.AI

How does Adversarial Influence Scale in Multi-Agent Systems?

Addison J. Wu, Jasin Cekinmez, Michel Liao et al. (+2 more)

Multi-agent deliberation can improve performance, but what happens when some agents do not act in good faith? In practice, an agent may be deceptive and work to subvert the group, whether through its own objectives or external instruction. We study how susceptibility to deception scales as groups increase in size and deceivers become more prevalent. It is not the number of agents in the group that matters, but the proportion of deceivers. We observe that the defection rate, how often initially correct agents switch to an incorrect final answer, rises linearly with this proportion. Whereas humans in comparable conformity studies are reliably swayed only when misleading confederates form a majority, LLM agents defect regularly even when deceivers remain a minority. Susceptibility also depends on which models are interacting, especially on the honest agent side. Unexpectedly, allowing deceivers to coordinate privately can make them less effective. Altogether, our results show that adding more agents is therefore not a sufficient defense, because the adversary can simply scale with the group.
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All authors Addison J. Wu, Jasin Cekinmez, Michel Liao, Karthik Narasimhan, Thomas L. Griffiths

Computers and Society

Quantifying AI impact in energy transitions: The Energy Justice Impact Assessment (EJIA) framework

Emily Bringmann, Florian Kutzner, Bianca Weber et al. (+1 more)

Artificial intelligence is increasingly deployed across energy systems to optimize efficiency, balance supply and demand, and integrate renewable sources. These applications alter how energy systems function, changing how benefits and burdens are distributed, whose needs are represented in system design, and who can influence decision-making, with consequences for energy justice that are rarely quantified. This paper addresses that gap through a systematic review of AI impact assessment approaches, followed by the development of a new framework. Reviewing 26 peer-reviewed frameworks using PRISMA methodology, we find that most address a single impact dimension, only three provide quantifiable indicators, and although 21 reference justice implications, only two operationalize justice-relevant constructs through measurable indicators. No existing framework combines multidimensional impact coverage with quantifiable indicators disaggregated by stakeholder group. We therefore introduce the Energy Justice Impact Assessment (EJIA) framework. Crossing four AI lifecycle stages - data collection, model development, deployment, and continuous adaptation - with the three tenets of energy justice (distributional, recognition, procedural) produces a map of where injustice can arise, which we use to derive a set of environmental, social, and economic outcome indicators. These are measured separately per affected stakeholder group and assessed against a counterfactual across defined phases, enabling observed effects to be attributed to the AI application. The EJIA framework is descriptive rather than normative: it makes differential impacts visible and comparable, while judging whether a pattern constitutes injustice remains a context-dependent decision for practitioners. An exemplary application to an AI-based demand-side response system in social housing demonstrates the framework's use.
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All authors Emily Bringmann, Florian Kutzner, Bianca Weber, Celina Kacperski

Computers and Society

Fair Feed Ranking for Participatory Budgeting

Carina I. Hausladen

In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power. We argue that fair exposure should therefore be treated as a democratic-design goal. We study Consul Democracy, a widely deployed open-source digital-democracy platform, and show that its proposal feeds are typically ordered by popularity, recency, or comment activity. Building on this diagnosis, we propose FairFeed, a feed-ranking design for PB that uses transparently declared preferences, boosts under-exposed proposals, and admits a rate-limited reject channel for crowd-sourced vetting. We evaluate the design in a simulation anchored in Munich's 2025 PB process and compare it with random, newest, and most-commented feeds. In this simulation, FairFeed broadens proposal discovery, distributes visibility more evenly across the eligible pool, increases cross-cutting support, and improves resistance to manipulation relative to comment-based ranking. We conclude by outlining the human-subjects evaluation needed to test whether onboarding can recover voter preferences accurately enough for deployment in practice.
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Computers and Society · Cross-listed from cs.SE

Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning

Qusay H. Mahmoud

Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students should be allowed to generate code, but whether software-engineering (SE) programs can preserve and assess human understanding while preparing students to work responsibly with increasingly capable AI systems. This paper presents a structured integrative review of research and practice from 2023 through 23 September 2026, supplemented by established work on AI literacy, technical debt, and human-AI collaboration. The evidence supports a conditional conclusion: GenAI can improve access to explanations, feedback, practice, and short-term task completion, but learning outcomes depend on prior knowledge, scaffolding, verification, task design, and assessment. We examine student help-seeking and authorship, faculty assessment and policy demands, and the wider SE risks created when generated artifacts persist across repositories, teams, architectures, and deployed systems. We extend the concept of comprehension debt: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software. We then refine the AI-Augmented Software Engineering Education (AASEE) framework into five non-linear integration levels and four cross-cutting evidence obligations: explain, verify, modify, and account. The framework links delegation to evidence, governance, and recovery mechanisms appropriate to its consequences.
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Computers and Society · Cross-listed from cs.CL

Where LLM Graders Succeed and Break: Evidence from Two Computer-Science Exams

Ali Habibullah, Yazan Alshoibi, Mohammad Alshiekh et al. (+2 more)

One long-form exam in a large course costs hundreds of grader-hours, and qualified graders are scarce; LLM graders are a tempting alternative. To show its pitfalls we grade a practical Computer Vision exam ($570$ dual-graded students) under $171$ configurations spanning closed and open-weights models; the best reaches mean absolute error $1.64/35$, below the $2.61/35$ two human graders achieve against each other. The catch is the prompt: a short ”strict grader” preamble drives $14$ of $17$ open-weights models out of the graded band ($\text{MAE} \ge 8$), three stopping grading altogether. The damage traces to the preamble's two credit-withholding sentences, not to tone or model scale; one of them, ”never give partial credit”, alone makes two of three probed models stop grading. The closed flagships of three vendors shift calibration under it but stay in the band. In $162$ further configurations on a second, independent Machine Learning exam from another course ($1{,}038$ dual-graded students), the preamble worsens ten models, moving three out of the band into collapse and one into refusal, yet improves seven whose neutral prompts over-mark: the vulnerability replicates, but its direction is exam-specific. Light LoRA fine-tuning repairs it: one adapter on the two exams' pooled $\sim 3{,}900$ graded examples brings five small open models to parity or better with a human grader in agreement with the grader pair, and sensitivity to the three harsh personas nearly vanishes ($\le 0.32$ MAE). We release the anonymised dataset, full ablation grid, and grading, fine-tuning and analysis pipelines.
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All authors Ali Habibullah, Yazan Alshoibi, Mohammad Alshiekh, Salman Khan, Naeemullah Khan