New papers on fairness, safety, alignment and governance.

arXiv:2607.23941v1 Announce Type: cross Abstract: Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features. Clustering analysis reveals 18 distinct bot types spanning content-specialized,

arXiv:2607.24275v1 Announce Type: cross Abstract: Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification. This challenge is particularly acute in digital phenotyping, where continuous behavioural data raises concerns around consent, privacy, and fairness. In this paper, we propose a computational ethical framework for AI-driven digital phenotyping system in whi

arXiv:2504.02636v3 Announce Type: replace Abstract: As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developing policies to govern its ethical, pedagogical, and institutional use. However, these policies vary across national and institutional contexts. We undertake a cross-nationalanalysis of GAI guidelines issued by leading universities in the United States, Japan, and China, identifying key policy orientations and proposing a structured frame

arXiv:2511.13949v4 Announce Type: replace Abstract: The rapid integration of AI writing tools into online platforms raises critical questions about their impact on content production and outcomes. We leverage a unique natural experiment on Change$.$org, a leading social advocacy platform, to causally investigate the effects of an in-platform ''write with AI'' tool. To understand the impact of the AI integration, we collected 1.5 million petitions and employed a difference-in-differences analysis

arXiv:2601.08878v2 Announce Type: replace Abstract: Text-based online counselling scales across geographical and stigma barriers, yet faces practitioner shortages, lacks non-verbal cues and suffers inconsistent quality assurance. Whilst artificial intelligence offers promising solutions, its use in mental health counselling raises distinct ethical challenges. This paper analyses three AI implementation approaches - autonomous counsellor bots, AI training simulators and counsellor-facing augmenta

arXiv:2603.18221v3 Announce Type: replace Abstract: Written work no longer certifies that a student understands it: a polished analysis now says little about who did the thinking. Oral examinations restore that evidentiary link, but they have never scaled, because conducting and grading them is expensive. We report on a system in which voice AI conducts a personalized oral exam and a council of three large language models (LLMs) grades the transcript, each model scoring independently and then re

arXiv:2603.20248v2 Announce Type: replace Abstract: AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust in algorithmic institutions dissipate and when they grow into collapse. Stability refers here to asymptotic recovery from finite state perturbations under fixed structural parameters. We address this gap by developing a mathematical framework for institutional trust stability that couples a Friedkin-Johnse

arXiv:2603.28371v2 Announce Type: replace Abstract: When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary, and that coherent explanation reliably signals both. I argue that this assumption fails for contemporary Large Language Models (LLMs). I introduce what I call the Bidirectional Coherence Paradox: competence and grounding not only dissociate but invert across epistem

arXiv:2605.02050v2 Announce Type: replace Abstract: This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies). Drawing on established practices from disciplines with established RCT traditions, including software engineering, economics, clinical and health sciences, and psychology, we synthesize five principles drawn from established validity frameworks and open-science standards on transparency, repeatability, and verification, which together

arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds. Our main argument is that even as a gap in Demographic Parity is used to diagnose inequality between groups, Equalized Odds constitutes a more reliable fairness crit

arXiv:2507.21134v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based

arXiv:2602.18443v2 Announce Type: replace-cross Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates eleven large language models generating six-word subject lines for German counselling emails through hierarchical assessment - first categorising outputs, then ranking within categories to enable manageable evaluation. Nine assessors (counselling professionals and AI systems) enable analysis via Kripp

arXiv:2605.20198v2 Announce Type: replace-cross Abstract: Augmented analytics has transformed how Business Intelligence (BI) systems support decision-making, shifting non-technical managers from manual analysis toward dependence on automated insights. Current BI research often overlooks the cognitive mechanisms and the direct impact of AI-enabled analytics on decision quality. This study employs the theory of cognitive delegation to investigate the association between trust in augmented analytic

arXiv:2606.09833v2 Announce Type: replace-cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work. Despite the considerable potential of human-agent collaboration both in preserving human agency and generating economic value, this paradigm remains largely absent from occupational task evaluation, hindered by the difficulty of gathering real human data and accounting for inter-human variability. We introduce CollabSkill, a framework for evaluating human

arXiv:2606.13385v2 Announce Type: replace-cross Abstract: LLM-based web agents are increasingly deployed in real-world settings such as e-commerce, where they interact extensively with untrusted web content while executing actions that carry direct financial consequences. This makes them vulnerable to prompt-injection attacks, in which seemingly benign web content conceals adversarial instructions that manipulate the agent's behavior. Existing security benchmarks adopt an \textit{attack-centric}

Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety blind spot: a harmful objective can be fragmented into locally plausible subtasks, allowing malicious intent to evade detection by any single agent. This is a growing social-impact challenge: systems handling sensitive information or consequential tools can turn routine delegation into unauthorized disclosure or unsafe action. We argue that this failu

Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety blind spot: a harmful objective can be fragmented into locally plausible subtasks, allowing malicious intent to evade detection by any single agent. This is a growing social-impact challenge: systems handling sensitive information or consequential tools can turn routine delegation into unauthorized disclosure or unsafe action. We argue that this failu

Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents enable a different recommendation process: a user specifies a need before choosing a platform, leaving platforms to compete for the user's attention, which we refer to as an agentic recommendation market. In our controlled LLM-based experiments across three product domains, we find this new setting of recommendation creat

Foundation models for 12-lead electrocardiograms (ECGs) transfer well across clinical tasks, but the physiological knowledge encoded in their representations remains opaque. We present CADENCE, a framework that decomposes an ECG foundation model into a human-interpretable, queryable dictionary of physiological concepts. Using a BatchTopK sparse autoencoder, CADENCE factorizes Layer-6 embeddings from more than nine million ECG tokens into 8,192 sparse cardiac atoms. These atoms align better than

Auto-bidding systems optimize bids to maximize value under efficiency constraints such as Cost-Per-Action (CPA). Existing methods treat each day as an independent episode. However, many advertisers produce value so sparsely that per-day efficiency ratios become statistically unreliable, undermining advertiser retention. Platforms therefore evaluate window-level efficiency over sliding windows of $W{=}7$ days, ensuring fair evaluation and long-term advertising effectiveness. This creates cross-ep

Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly relying on their outputs. However, the deep integration of open-source model sharing ecosystems with LLM-powered critical decision-making applications also introduces critical risks: if an attacker can manipulate the model's cognitive stance, they can indirectly influence the judgments and actions of downstream decision-makers. This paper defines s

Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing methods typically regard SIDs as independent discrete symbols, while often overlooking the topology of the learned semantic ID space. We identify a structural mismatch between tokenization and generation: the tokenizer learns a structured code space with semantic neighborhood relations, whereas the generator consumes sem

Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. However, these artificial codes may overcompress item semantics, remain misaligned with pretrained LLM vocabularies, and require costly autoregressive decoding. In light of this, we propose VaLiDRec, a generative recommendation framework based on variable-length, LLM-aligned semantic identifiers. VaLiDRec constructs SIDs directly from informative nat

Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality is limited by static, task-agnostic reward models. This mismatch leads to sparse learning signals and suboptimal alignment. We introduce MeRLa (Meta-Learned Reward Shaping), a principled framework that meta-learns a task-aware shaping function $Φ(x,y;φ)$ across auxiliary tasks before RLHF training. The learned shaping produces a composite reward th

Big Data & Society, Volume 13, Issue 3, July-September 2026. The COVID-19 pandemic witnessed the transformation of data from a public health resource into a measure of morality. Disadvantage indices such as the Healthy Places Index and other metrics became central to promoting equity in the pandemic response, ...

Quantum computing (QC) has established itself as a disruptive technology that has the potential to enhance computational capabilities across next-generation energy systems. Its integration into smart grids can enable intelligent decision-making, secure control mechanisms, and advanced optimization strategies. However, existing research remains methodologically fragmented and lacks a unified discussion for practical adoption. This highlights the need for a systematic assessment of the current res

IntroductionBreast cancer remains one of the leading causes of cancer-related mortality rate worldwide, and the identification of effective drug combinations is an essential requirement in pharmaceutical research. The integration of Artificial Intelligence (AI) in processing large volumes of chemical and biological data combines molecular representation, predictive modeling and structured support within a single accessible tool, which accelerates early-stage candidate identification for breast c

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, and early identification of malignant abnormalities plays an important role in improving patient survival rates. However, accurate lung cancer classification using CT imaging remains challenging because of limited dataset availability, class imbalance, overlapping lesion characteristics, and lack of interpretability in existing deep learning systems. This study presents a GenAI-driven CNN–RNN framework for explaina

IntroductionBanana production is increasingly threatened by fungal diseases such as Fusarium wilt and Black Sigatoka, posing severe risks to food security and agricultural economies. Recent image-based approaches using deep learning have shown high predictive capacity for plant disease recognition; however, their limited transparency, calibration uncertainty, and sensitivity to domain shifts can restrict their use in decision-support workflows that require auditability.MethodsThis study proposes

Research on preference optimization often varies the training objective while holding the data fixed. We instead ask whether a small, high-confidence set of on-policy responses can provide a reliable learning signal. Our method, DMAPO (Data-centric Multi-evaluator Agreement for Preference Optimization), generates candidate responses from the target policy, evaluates helpfulness, factuality, and conciseness with rubric-specialized evaluators, applies a process-critic correction, and retains only

Empirical user studies are essential for evaluating visual encodings and can reveal perceptual and cognitive mechanisms, but they do not by themselves provide causal, predictive accounts of interpretation errors. Evaluations are therefore often post hoc: they measure performance after a design has been specified rather than predicting how attention, uncertainty, memory, and bias may produce accurate or erroneous judgments. To address this mechanistic gap, we translate a cognitive theory of visua

Urban decision-making requires integrating heterogeneous spatial data. While current GIS tools handle geometric computation efficiently, they lack the semantic reasoning to guide complex workflows. Analysts manually manage data discovery, spatial boundaries, and measurement semantics, risking aggregation errors. We present UrbanTrace, a visual analytics system that transforms manual spatial data-wrangling into a transparent, node-based collaborative workflow with context-aware AI agents. Using a

The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated. In the State-of-the-Art (SOTA) research, fifteen (model, corpus) configurations were trained using Proximal Policy Optimization (PPO). The experiments included Pythia-70M, 160M, 410M and SmolLM2-135M, 360M on the TinyStories, CNN/DailyMail, and Wikitext-103 corpora. Three reprod

Interactive electrical muscle stimulation (EMS) has revealed its promise as a portable interface for force-feedback. However, while much ink has been spilled about the advantages of EMS, few have investigated one of its central limitations: the need to attach electrodes to users. This has dramatically limited the application of EMS, especially in brief interactions or physical assistance with tools. To explore an alternative, we propose embedding electrodes (and stimulator) inside objects that t

Developers judge a model checkpoint by how it behaves. After supervised fine-tuning (SFT), two checkpoints that perform about the same across relevant benchmarks are treated as interchangeable, equally ready for the next alignment stage, typically preference optimization. We ask whether this judgment misses a pretraining imprint: a difference that no post-SFT benchmark reveals, yet that decides how each checkpoint responds to further training. To find out, we run a controlled experiment on the f

As conversational AI systems become increasingly integrated into daily life, their potential effects on user well-being require ongoing attention. While consumer-facing generalist models can provide benefits, including improved access to information, learning, productivity, self-reflection, and companionship, they also introduce risks, such as emotional entanglement, unhealthy dependence, and the amplification of psychological vulnerabilities. Drawing on prior research and empirical observations

Background: Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools

Extended Reality (XR) is increasingly used in human-robot interaction to communicate robot intent, planned motion, reachability, and state. We argue that XR should also be understood as a mediation layer for situated human control in human-robot teaming. Situated human control denotes the human collaborator's ability to understand, shape, authorize, and interrupt robot action within the concrete physical, social, and temporal context in which that action unfolds. We ground this perspective in sc

A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vector embeddings of text, images, or audio. These systems return similarity scores through their APIs for ranking and verification. However, such releases can leak information about individual records and enable membership inference attacks. While differential privacy (DP) provides a principled metric for quantifying attack risks, naïve application of