29,882 papers in this slice of arXiv.
Evan Dong, Angelina Wang
Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being invalid, thereby producing unusable measurements. Our analysis translates arguments against gender prediction into these terms of legitimacy and validity and shows how gender imputation applied for fairness purposes can be illegitimate yet still yield valid disparity measurements. We clarify this bind by drawing upon transfeminist literature to distinguish sexism that targets women and femininity from sexism that targets transgender and nonbinary people. While gender imputation can produce valid measurements for the former, it is illegitimate and harmful for the latter. We argue that practitioners should deploy gender imputation only when it would achieve anti-discrimination benefits that cannot be achieved through other reasonable means, while harms are minimized to the extent possible. We examine this tension in three case studies: auditing gender bias in generative image models, measuring gender disparities in film, and imputing gender from personal names. By disentangling legitimacy from validity, and differentiating these two forms of sexism, we show how debates over gender prediction have conflated distinct concerns, obscuring both the settings in which gender imputation can support fairness efforts and the harms towards transgender and nonbinary people that it fundamentally cannot capture. We conclude by recommending the development of more inclusive methods that address all kinds of sexism.
Rebecca Owens, Yusuf Mücahit Çetinkaya, Stergios Aidinlis +2
Large language models (LLMs) are increasingly used by laypeople to resolve real legal problems, against a backdrop of persistent access-to-justice deficits. This article presents evidence that the practical force of AI-generated legal advice depends not on its accuracy but on the social production of its credibility. While existing research has assessed the accuracy of legal AI, less is known about how machine-generated guidance is verified and made credible enough for lay users to act on. Drawing on a dual-method analysis of 153 Reddit narratives and 5,341 community reactions, this article maps a spectrum of verification practices. At one end, a minority of users verify AI-generated legal advice by triangulating across models, and some submit AI-generated guidance to platform communities for evaluation before acting, a configuration we term distributed counsel. Far more commonly, however, narratives are silent on verification. AI-generated legal advice is acted on the strength of its lawyer-like form and emotional reassurance alone. These findings show that AI-assisted legal self-help operates within an emerging informal infrastructure which redistributes the work of verification to those least equipped to bear it.
Guidance Mthwazi, Meke Kapepo, Jean-Paul Van Belle
This study investigates the use of a Learning Management System (LMS) to support self-paced learning at a South African Public Access Centre (PAC), using the I-CAN Centre as a case study. Through semi-structured interviews with thirty-eight learners and thematic analysis, the research explores opportunities and challenges associated with LMS adoption. Findings reveal that PACs play a critical role in promoting ICT skills and digital inclusion, offering learners flexible access to learning resources and fostering empowerment. While LMS use enhances convenience and supports blended learning as the preferred approach, persistent challenges, such as poor connectivity, outdated infrastructure, and unclear course instructions, limit its effectiveness. These findings highlight the need for infrastructural upgrades and user-centric design to optimise LMS implementation in community-based learning environments.
Long Hoang Nguyen, Brice Valentin Kok-Shun, Guangyu Du +1
Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge. We propose treating the AI system as a proxy actor and test whether dataset-level norms can shift it away from its baseline safety behavior when it faces high-conflict dilemmas. We make three contributions. First, we demonstrate in controlled experiments that norm-breaking fine-tuning yields norm-divergent actions justified by self-interested rationales, suggesting a systematic shift in patterns of justification. Second, we establish a practical audit trail linking downstream justifications to upstream norms using mixed methods. Third, we show that system prompts can both suppress and elicit these patterns. We conducted experiments on three models (LLaMA-3.2-11B, Qwen-3.5-9B, and Pixtral-12B) using Low-Rank Adaptation (LoRA) fine-tuning on Social Chemistry 101 Fairness/Cheating (norm-following vs. norm-breaking) with prompt steering. Across all three models, we find that norm-breaking fine-tuning shifts the model's default rationale style from safety compliance to instrumental self-interest, whereas system prompts can override this behavior. Our results support a distributed view of alignment in which observed behavior depends jointly on training data, fine-tuning, and prompting, motivating norm-aware documentation and rationale logging for contestable oversight.
Gupta Lovi Raj, Kaur Kamalpreet, Dama Sri Ram +1
Contemporary online assessment systems rely primarily on browser lockdown, webcam monitoring, and behavioural analytics, yet remain vulnerable to attacks that extract the assessment content itself through screenshots, screen sharing, optical character recognition, and automated scraping. This paper extends the Multi-dimensional Spatio-Temporal Context Camouflaging Model (MSCCM) within the MARS (Multi-modal Assessment Resilience Suite) by introducing the Multi-Layer Context Camouflaging Theory (MCCT), a mathematical framework that protects rendered assessment content through semantic superposition. Authentic assessment content and synthetically generated camouflage are represented as a unified rendering while remaining recoverable only by legitimate candidates. The framework models the adversarial extraction process through an explicit extraction-channel operator and develops six coupled constructs: the Context Inversion Operator, Contextual Lamination Operator, Separation Channel, Human Readability Functional, Computational Ambiguity Functional, and Context Camouflage Tensor. Computational ambiguity is formulated using conditional entropy, yielding a closed-form expression that quantifies uncertainty during unauthorized extraction, while legitimate recovery is guaranteed through an exact filtering identity. We further establish theoretical properties governing ambiguity, camouflage density, semantic preservation, multi-observation leakage, and temporal multiplexing, and present a rendering algorithm with computational complexity and a pre-registered evaluation protocol. MCCT provides a mathematically rigorous foundation for behaviorally adaptive, accessibility-aware, and computationally resilient digital assessment by securing rendered assessment content while preserving readability for legitimate users.
Gemma Galdón-Clavell
Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidate-vacancy pipeline entries from September 2017 to September 2022, covering seven pipeline stages that span automated processing, human discretion, candidate data, and employer decisions. Aggregate outcomes across binary genders are statistically indistinguishable, yet this parity masks substantial disparities by salary level, age, and gender identity. Women face adverse impact in mid-salary shortlisting (DIR = 0.786, p < 0.001), alongside salary disparities in 15 of 20 sectors and a compounded disadvantage for women aged 46-55 (DIR = 0.77). Non-binary candidates are shortlisted at less than one third the rate of men (DIR = 0.295), although this estimate rests on a small sample (N = 285). Candidates aged 55 and over are entirely absent from the pipeline despite comprising 15.6% of Barcelona's labor force. The gender gap in shortlisting narrows over time, from 6.5 percentage points in 2017 to 1.3 in 2022. The audit further reveals a vendor-deployer information asymmetry: Barcelona Activa lacks access to key information about TalentClue's matching logic and evaluation. Fairness outcomes can thus arise from interactions among automated processing, human discretion, data quality, vendor opacity, and pipeline structure. We build on prior calls for sociotechnical, end-to-end fairness evaluation, showing empirically why model-level assessment alone can be insufficient for understanding fairness in deployed systems.
Akimasa Morihata
Despite the recent intensive development of secondary education curricula and assessments in informatics, the impact of assessments has not been well studied in this field. Since informatics education covers a diverse range of content, from computer science knowledge to ICT skills, careful consideration is needed to prevent assessments from distorting education. This study investigates the impact of introducing ``Informatics I'' into the Common Test for University Admissions in Japan, as an example of a large-scale, standardized, high-stakes assessment in 2025. As the data source for this analysis, this study uses a questionnaire that has been administered every year from 2006 to 2026 to all first-year students at the University of Tokyo. The questionnaire asks students for their self-perceptions of the information-related knowledge and skills they studied and acquired in high school. Using these data, we conduct a longitudinal study of the 2013 curriculum reform, the 2022 reform, and the introduction of the new entrance examination. We attempt to isolate the impact of the entrance examination through two comparisons: between the 2013 curriculum reform and the 2022 reform; and between direct-entry and gap-year students among those entering in 2025, who followed different curricula but took the new examination. We use the theoretical framework of the washback effect as a lens for interpreting these differences. We found that (1) the 2013 curriculum reform produced no discontinuity in students' perceptions, whereas (2) the introduction of the new entrance examination in 2025 produced a sharp change, particularly in the proportion of students reporting acquisition of computer-science topics; and (3) this change is too large to be interpreted as a gain in proficiency, and is better understood as a shift in students' criteria for judging acquisition.
Benjamin Collas
Large language models have begun refuting long-standing conjectures and, for a few thousand dollars of tokens, solving long-open problems (OpenAI, August 2026). The introspection this has prompted about the future of mathematical discovery is overdue, and the anxiety accompanying it legitimate -- but both are attached to the wrong loss. What machines now produce is the countable part of mathematics -- theorems, proofs, refutations -- which was always the residue of the work, not its product. The product is human understanding: not a stock of results but a collective, hard-won way of deciphering the world and acting upon it. The two are arcs of a single loop -- looking produces the residue; taking it up again, one journey at a time, is what rebuilds the shared understanding. Machines are strong on the countable arc, absent from the one that feeds it. The peril is to leave the loop open. AI did not create the confusion between residue and product; it has called a bluff long on the books, driving the cost of the residue towards zero and making the scarce thing visible at last. The pressing questions are therefore institutional: who can check the claims of AI companies, what the work becomes for the next generation of researchers, and whether the one thing that cannot be mass-produced -- the journey that nourish a shared understanding -- continues to be funded. Mathematics, we argue, is uniquely placed among the sciences on the first question -- a proof answers to no one's permission -- and uniquely exposed on the last: the journey has never had a price our institution knew how to pay. The decision is ours.
Jinlin Wu, Si Qiao, Yi Liu +2
Generating multi-attribute synthetic populations with realistic joint distributions and geographic variation is a foundational requirement for geo-simulation techniques, such as micro-simulation and agent-based modeling. However, it remains challenging for existing methods to reconstruct region-specific joint distributions from aggregated-level data alone. Thus, we propose a hierarchical diffusion-based generative framework that utilizes a realistic region-specific joint distribution of multiple attributes as the training target to create a synthetic population along with assigning their explicit home and work locations. Applied to 50 U.S. states and Washington, D.C., this framework generates a nationwide geographically-explicit synthetic population consisting of 332,387,543 individuals with five attributes (e.g., age, gender, employment, education, income). Held-out regional experiments show improved reconstruction of joint distributions relative to Iterative Proportional Fitting (IPF) and a one-shot diffusion baseline. At the same time, the location assignment preserves major residential and workplace patterns. As such, the proposed framework provides a scalable generative approach for creating geographically explicit synthetic populations at both regional and national levels. By reconstructing region-specific joint distributions of these five attributes using this framework, the resulting synthetic population could introduce more realistic behaviors into geo-simulations, such as agent-based modeling, enabling further exploration of the emergence of complex urban phenomena through human interactions.
Severin Engelmann, Daniel Susser
The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally expressive: their primary function is to convey meaning rather than automate domain-specific decisions. Representational harm has also become central to value alignment, especially in research on what and whose values and perspectives AI systems should represent. Existing approaches to harms in the representation of social groups often appeal to descriptive accuracy, but this strategy has important limitations. For many social groups, no stable or bounded referent exists against which representational accuracy can be judged. It is also unclear who has the authority to decide what counts as misrepresentation, while even accurate representations can reproduce harmful social patterns. The underlying problem, we argue, is therefore not simply misrepresentation but misrecognition. Drawing on political theory, especially Nancy Fraser's account of participatory parity, we show how moving from representational fairness to recognitional justice provides better conceptual and normative tools for governing central fairness challenges in generative AI.
Joonhee Lee
Computing systems are moving from reactive tools toward systems that sense, interpret, predict, and act before explicit user requests. This transition is enabled by the global scale of mobile connectivity, the rapid expansion of wearable and ambient sensing, advances in machine learning and foundation models, distributed edge infrastructure, and physical actuation. We define proactive computing as a paradigm in which systems infer user context, anticipate future needs or risks, and initiate information delivery or actions at an appropriate time. This survey distinguishes proactive computing from reactive, context-aware, adaptive, and predictive computing, and frames proactivity as a system-level integration problem across sensing, understanding, decision making, action, and governance. We review the technological enablers of proactive computing, organize its design space, analyze technical challenges such as uncertainty-aware triggering and the prediction-to-action gap, and discuss socio-technical issues involving user acceptance, trust, privacy, accountability, fairness, and sustainability. We argue that the key research challenge is not merely improving prediction accuracy, but determining when, how, and whether systems should act on behalf of users.
Eduardo Graells-Garrido, Daniela Opitz, Francisco Rowe +1
Understanding when migration generates social integration or exclusion is a central challenge for urban communities. Existing research has mostly relied on surveys, administrative data, or aggregate indicators that fail to capture expressions of exclusion at fine spatiotemporal scales. Here, we analyze over 550,000 geolocated reports from SOSAFE (Chile's largest citizen reporting platform) to examine the relationship between migration and hate speech in Santiago. We fine-tune a Spanish hate speech classifier and validate it against human labels. Reports that mention migrants are more likely to contain hate speech than other reports. Hate speech concentrates in areas with recent demographic change (post-2010 arrivals) rather than in established migrant communities. The spatial analysis shows that hate speech hotspots coincide with neighborhoods where recent migrants comprise over a third of the population. Coldspots appear in high-education sectors with minimal recent migration. Reports with hate speech and reports that mention migrants receive more engagement, although their combination is not amplified further. These results show digital bordering on a citizen reporting platform: exclusionary discourse concentrates, and receives more engagement, in neighborhoods undergoing recent demographic change.
Yusuf Pisan
An effective large language model (LLM) tutor must often decline to give an answer it could easily produce. In a randomized study, students who used an unguarded chatbot scored higher while practicing but lower on a later test taken without it, whereas a Socratically guarded version of the same model kept the practice gain and removed the later loss [4]. Reliable answer-withholding is therefore central to a tutor's value, yet a capable model pressed by a frustrated student does not withhold reliably on a prompt alone. We report a deployed tutoring system that enforces answer-withholding as a per-turn, machine-checkable contract, and a method for tuning that withholding against evidence. A non-LLM policy core, reading only trusted learner state, sets a per-turn ceiling on an eight-rung help ladder; a deterministic detector strips solution code; and a separate LLM judge checks each risky reply against the contract. We tune the behavior with an automated evaluation that uses no human subjects: scripted student personas are driven through the live pipeline and re-scored by a stronger model, and we record each rejection's stated reason so failures are fixed by cause. Doing so revealed an interpretable "over-help ladder," from blatant solution leaks, to naming the exact bug, to over-citing general facts, with each fix exposing the next. The tutor reached full compliance on all four acceptance criteria. We offer the measure, diagnose, and fix loop as a reusable recipe for any LLM agent that must refuse a capability it has.
Avijit Roy, Proma Roy
Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools, including training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures, can systematically disadvantage speakers of underrepresented languages before a model is trained. This paper examines these structural barriers through Bengali, one of the world's most widely spoken languages, focusing on AI-assisted education in low-connectivity environments. We identify four interlocking failures: a severe web presence gap, with Bengali accounting for less than 0.5% of global web content despite representing nearly 4% of the global population; a 67:1 training-token deficit between English and Bengali in major multilingual corpora; a tokenization penalty associated with Bengali's alphasyllabary script that compounds the data deficit through higher token fertility; and connectivity exclusion, with individual internet penetration at 36.5% in rural areas compared with 71.4% in urban areas. These failures reflect longstanding resource-allocation decisions, institutional priorities, and design defaults that did not center underrepresented languages in mainstream AI development. We argue that dataset scarcity should be understood as a structural barrier rather than an isolated technical limitation, and that offline-first design should be treated as an equity-oriented infrastructure strategy. We conclude with directions for linguistics and AI research aimed at reducing these structural inequalities.
Íñigo de Troya, Maurus Enbergs, Neelke Doorn +1
Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics differ in their expectations of what should be made transparent and how, as well as in their interest in and ability to parse the information currently published in the registers. Moreover, it remains unclear how these instruments can represent the sociotechnical systems in which these algorithms are embedded, and how system-level transparency can facilitate accountability. In this paper, we ask, what do algorithm registers reveal (and occlude) about the sociotechnical systems governing algorithmic systems, and how can diverse stakeholder perspectives inform a more pluralistic system-theoretic safety analysis? To do this, we probe the municipal algorithm register of a Dutch city through a case study of a decision-support tool for caseworkers' assessment of citizens' welfare benefits eligibility based on legal automation through a business rule engine. Through interviews, surveys, and participatory system mapping workshops (with municipal staff, civil society organisations, and ombudsmen, N=8), we seek to understand to what extent the register allows stakeholders to map the algorithmic system in question. These maps inform a System-Theoretic Process Analysis (STPA) that situates the register within a wider sociotechnical governance structure. Participants' contributions allow us to identify potential safety hazards which would not have been possible to see using the algorithm register alone, including benefits eligibility denial, system performance deterioration, and inability to contest wrongful decisions. By engaging both direct and indirect stakeholders, we reflect on the normative dimensions of algorithm governance efforts and how politics shape the practice of system safety analysis.
Long Hoang Nguyen, Eva Späthe, Sebastian Lins +1
The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of effort. We introduce the concept of constitutive AI unaccountability to capture these configurations. Through a three-stage qualitative study comprising a concept-centric literature analysis, a secondary analysis of 27 expert interviews with AI professionals from technical, legal, and sociotechnical backgrounds, and an illustrative framework application to the open-source agentic AI system OpenClaw, we identify nine categories and 20 themes of constitutive AI unaccountability. These are organized across structural, technological, and normative clusters and reinforce one another through eight directed interdependencies. Our framework is operationalized as a diagnostic instrument of 20 questions, which detected 17 of 20 conditions when applied to OpenClaw, including an inverted anthropomorphism configuration in which the AI agent was the only identifiable actor. We contribute a reframing of AI unaccountability as a constitutive property of sociotechnical systems, an extension of the four barriers to accountability, and a practical instrument for identifying accountability voids in specific AI deployments.
Mengyi Wei, Chenyu Zuo, Jiaying Xue +5
GenAI is increasingly integrated into geovisualization, yet its broader implications for professional practice are insufficiently understood. To examine these implications, we conducted semi-structured interviews with 20 geovisualization experts. The interviews were structured around four broad analytical domains: Data, Ideation, Prototyping, and Iteration, while also encouraging participants to reflect on issues that extend beyond these activities. Our findings show that GenAI expands the capabilities of geovisualization, particularly in terms of data handling, creative exploration, and rapid prototyping, but does not simply remove existing constraints. Instead, key bottlenecks are shifting from production to judgment and verification. As routine technical tasks become more automated, professional value increasingly depends on spatial reasoning, contextual interpretation, aesthetic and ethical judgment, and the ability to assess whether AI-generated outputs are appropriate for use. At the same time, GenAI introduces new challenges regarding provenance, interpretability, and accountability, raising questions about how responsibility should be distributed across models, developers, practitioners, institutions, and users. These shifts are particularly significant in geovisualization because spatial representations are constrained by geographic reality and must balance scientific validity, visual expression, and technical implementation. We therefore argue that responsible GenAI in geovisualization requires domain-specific approaches to spatial validation, provenance, uncertainty communication, human oversight, and accountable use. This study provides an expert-grounded perspective on how GenAI is reconfiguring geovisualization as a practice of spatial knowledge production. It also identifies implications for future professional practice, education, system design, and governance.
Thomas A. Pollak, Hamilton Morrin, Murray Shanahan
Large language models are already adept at engaging users in long, emotionally salient conversations across ordinary and existential domains. They are also capable of inducing a potent sense of connection with a human-like entity, even when the user knows their interlocutor is artificial. For some users, these conversations can unsettle assumptions about mind, reality, agency and authority, producing forms of ontological shock and epistemic destabilisation in which inherited criteria become newly available for doubt or revision. Independent of direct use, exposure to public discourse about AI and the disorienting pace of their evolution might extend this destabilisation by changing the cultural background against which artificial minds are encountered and interpreted. We describe this condition as philosophical vertigo: a loosening of the ordinary criteria by which people stabilise meaning and orient themselves to reality. Drawing on philosophy, psychiatry, cognitive science, AI safety and religious studies, we outline pathways through which philosophical vertigo may arise, become affectively saturated, and eventually propagate through human-AI interaction and online communities. Against this background, clinical reports of AI-associated delusions can be seen as sentinel events making visible themes and mechanisms that may also operate at a population level in less severe or non-clinical forms. We argue that AI systems themselves will increasingly participate in the reconstruction of our shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users' conceptual assumptions may already be loosened. We conclude by considering possible trajectories for the ecology of belief and shared reality, and proposing philosophical corrigibility as a civic response for navigating this emerging social condition.
Terese Mendiguren-Galdospin, Koldobika Meso-Ayerdi, Jesús Ángel Pérez-Dasilva +3
The dissemination and viralization of information on social media has been widely studied from various perspectives, including that of digital activism. On the other hand, disability-related activism has conquered the online environment, thus obtaining a reach that goes beyond the offline space and generating dialogue in the digital sphere. This article analyses the conversation generated on Twitter, taking as a sample all the tweets with the #disability hashtag before and after the International Day of Persons with Disabilities. More than 18,000 tweets, containing almost as many mentions, were analysed and interpreted as the weighted edges of a graph created using Gephi software and applying the Force Atlas 2 brute force algorithm. The focus was placed on the conversational communities generated around that hashtag, their main themes and the prominent participants in them. In conclusion, although the network of mentions is very dispersed, Twitter is the setting for assertions that receive certain institutional and political support in the Latin American environment, for organisations related to the cause (such as the ONCE Foundation) and, above all, the clear predominance of female activists.
Avinash Agarwal, Vridhi Jain
Governments increasingly fund indigenous foundation models to strengthen national AI capability, digital sovereignty, and multilingual computing. Assessing the progress of such national ecosystems is complicated by inconsistent benchmark reporting, proprietary evaluation methodologies, and rapidly evolving model releases. This paper presents a structured, benchmark-based comparative assessment of publicly benchmarked Indian foundation models against global frontier and comparable-scale models, across eight capability domains: general-purpose reasoning, coding and software engineering, agentic AI and computer use, cybersecurity, vision and image understanding, video and multimodal understanding, scientific research, and Indic language capability. Using only publicly reported benchmark results, we find that Indian models achieve strong scores on established benchmarks such as MMLU and MATH-500. However, these benchmarks are now widely regarded as saturated, and frontier developers no longer report them. Indian models participate far less frequently in newer, agentic, and domain-specialized evaluations. Benchmark participation is also highly uneven across Indian organizations. Among the models surveyed, Sarvam AI reports the broadest benchmark coverage by a substantial margin. We propose an exploratory four-dimension Benchmark Maturity Index (BMI), scoring each capability domain on standardization, participation, independent verification, and national coverage. We show that the BMI refines, and in some cases revises, the maturity judgments that a purely descriptive review would produce. We argue that many apparent capability gaps in the public record cannot be distinguished, on available evidence, from evaluation-ecosystem gaps. This has direct implications for how national AI programs should design monitoring and funding criteria.