mstyslavity/prisma_v3 huggingface dataset:
Despite the continued anthropomorphization of AI systems, the potential impact of racialization during human-AI interaction is understudied. This study explores how human-AI cooperation may be impacted by the belief that data used to train an AI system is racialized, that is, it was trained on data from a specific group of people. During this study, participants completed a human-AI cooperation task using the Pig Chase game. Participants of different self-identified demographics interacted with AI agents whose perceived racial identities were manipulated, allowing us to assess how sociocultural perspectives influence the decision-making of participants in the game. After the game, participants completed a survey questionnaire to explain the strategies they used while playing the game and to understand the perceived intelligence of their AI teammates. Statistical analysis of task behavior data revealed a statistically significant effect of the participant's demographic, as well as the interaction between this self-identified demographic and the treatment condition (i.e., the perceived demographic of the agent). The results indicated that Non-White participants viewed AI agents racialized as White in a positive way compared to AI agents racialized as Black. Both Black and White participants viewed the AI agent in the control treatment in a negative way. A baseline cognitive model of the task using ACT-R cognitive architecture was used to understand a cognitive-level, process-based explanation of the participants' perspectives based on results found from the study. This model helps us better understand the factors affecting the decision-making strategies of the game participants. Results from analysis of these data, as well as cognitive modeling, indicate a need to expand understanding of the ways racialization (whether implicit or explicit) impacts interaction with AI systems.
Explainable AI (XAI) has become a crucial component of Clinical Decision Support Systems (CDSS) to enhance transparency, trust, and clinical adoption. However, while many XAI methods have been proposed, their effectiveness in real-world medical settings remains underexplored. This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. By categorizing existing works based on XAI methodologies, evaluation frameworks, and clinical adoption challenges, we offer a structured understanding of the landscape. Our findings reveal key challenges in the integration of XAI into healthcare workflows and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.
The safe use of pharmaceuticals in food-producing animals is vital to protect animal welfare and human food safety. Adverse events (AEs) may signal unexpected pharmacokinetic or toxicokinetic effects, increasing the risk of violative residues in the food chain. This study introduces a predictive framework for classifying outcomes (Death vs. Recovery) using ~1.28 million reports (1987-2025 Q1) from the U.S. FDA's OpenFDA Center for Veterinary Medicine. A preprocessing pipeline merged relational tables and standardized AEs through VeDDRA ontologies. Data were normalized, missing values imputed, and high-cardinality features reduced; physicochemical drug properties were integrated to capture chemical-residue links. We evaluated supervised models, including Random Forest, CatBoost, XGBoost, ExcelFormer, and large language models (Gemma 3-27B, Phi 3-12B). Class imbalance was addressed, such as undersampling and oversampling, with a focus on prioritizing recall for fatal outcomes. Ensemble methods(Voting, Stacking) and CatBoost performed best, achieving precision, recall, and F1-scores of 0.95. Incorporating Average Uncertainty Margin (AUM)-based pseudo-labeling of uncertain cases improved minority-class detection, particularly in ExcelFormer and XGBoost. Interpretability via SHAP identified biologically plausible predictors, including lung, heart, and bronchial disorders, animal demographics, and drug physicochemical properties. These features were strongly linked to fatal outcomes. Overall, the framework shows that combining rigorous data engineering, advanced machine learning, and explainable AI enables accurate, interpretable predictions of veterinary safety outcomes. The approach supports FARAD's mission by enabling early detection of high-risk drug-event profiles, strengthening residue risk assessment, and informing regulatory and clinical decision-making.
Modelling human variation in rating tasks is crucial for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using natural language value profiles -- descriptions of underlying values compressed from in-context demonstrations -- along with a steerable decoder model that estimates individual ratings from a rater representation. To measure the predictive information in a rater representation, we introduce an information-theoretic methodology and find that demonstrations contain the most information, followed by value profiles, then demographics. However, value profiles effectively compress the useful information from demonstrations (>70% information preservation) and offer advantages in terms of scrutability, interpretability, and steerability. Furthermore, clustering value profiles to identify similarly behaving individuals better explains rater variation than the most predictive demographic groupings. Going beyond test set performance, we show that the decoder predictions change in line with semantic profile differences, are well-calibrated, and can help explain instance-level disagreement by simulating an annotator population. These results demonstrate that value profiles offer novel, predictive ways to describe individual variation beyond demographics or group information.
The first voice timbre attribute detection challenge is featured in a special session at NCMMSC 2025. It focuses on the explainability of voice timbre and compares the intensity of two speech utterances in a specified timbre descriptor dimension. The evaluation was conducted on the VCTK-RVA dataset. Participants developed their systems and submitted their outputs to the organizer, who evaluated the performance and sent feedback to them. Six teams submitted their outputs, with five providing descriptions of their methodologies.
Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, these agents must be able to explain their behavior. However, state-of-the-art agents are typically driven by black-box models like deep neural networks, limiting their interpretability. We propose a method for generating natural language explanations of agent behavior based only on observed states and actions -- without access to the agent's underlying model. Our approach learns a locally interpretable surrogate model of the agent's behavior from observations, which then guides a large language model to generate plausible explanations with minimal hallucination. Empirical results show that our method produces explanations that are more comprehensible and correct than those from baselines, as judged by both language models and human evaluators. Furthermore, we find that participants in a user study more accurately predicted the agent's future actions when given our explanations, suggesting improved understanding of agent behavior.
Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust outlier detection remains unclear. This study compares an SPM approach with an explainable machine learning (ML) approach to establish transparent quality-control pipelines for plantar pressure datasets. Data from multiple centers were annotated by expert consensus and enriched with synthetic anomalies resulting in 798 valid samples and 2000 outliers. We evaluated (i) a non-parametric, registration-dependent SPM approach and (ii) a convolutional neural network (CNN), explained using SHapley Additive exPlanations (SHAP). Performance was assessed via nested cross-validation; explanation quality via a semantic differential survey with domain experts. The ML model reached high accuracy and outperformed SPM, which misclassified clinically meaningful variations and missed true outliers. Experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy, though SPM was assessed less complex. These findings highlight the complementary potential of SPM and explainable ML as approaches for automated outlier detection in plantar pressure data, and underscore the importance of explainability in translating complex model outputs into interpretable insights that can effectively inform decision-making.
Public funding processes demand fairness, learning, and outcomes that participants can understand. We introduce Komitee Equal Shares, a priceable virtual-budget allocation framework that integrates two signals: in voter mode, participants cast point votes; in evaluator mode, small groups assess proposals against collectively defined impact fields. The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing voting receipts. We deployed the framework in the 2025 Kultur Komitee in Winterthur, Switzerland. Our contributions are: (1) a clear separation of decision modes, addressing a gap in social choice that typically treats participatory budgeting as preference aggregation while citizens also see themselves as evaluators; and (2) the design of voting receipts that operationalise priceability into participant-facing explanations, making proportional allocations legible and traceable. The framework generalises to participatory grant-making and budgeting, offering a model where citizens act as voters and evaluators within one proportional, explainable allocation.
Large Language Models (LLMs) are known to overuse certain terms like "delve" and "intricate." The exact reasons for these lexical choices, however, have been unclear. Using Meta's Llama model, this study investigates the contribution of Learning from Human Feedback (LHF), under which we subsume Reinforcement Learning from Human Feedback and Direct Preference Optimization. We present a straightforward procedure for detecting the lexical preferences of LLMs that are potentially LHF-induced. Next, we more conclusively link LHF to lexical overuse by experimentally emulating the LHF procedure and demonstrating that participants systematically prefer text variants that include certain words. This lexical overuse can be seen as a sort of misalignment, though our study highlights the potential divergence between the lexical expectations of different populations -- namely LHF workers versus LLM users. Our work contributes to the growing body of research on explainable artificial intelligence and emphasizes the importance of both data and procedural transparency in alignment research.
Arabic-language patient feedback remains under-analysed because dialect diversity and scarce aspect-level sentiment labels hinder automated assessment. To address this gap, we introduce EHSAN, a data-centric hybrid pipeline that merges ChatGPT pseudo-labelling with targeted human review to build the first explainable Arabic aspect-based sentiment dataset for healthcare. Each sentence is annotated with an aspect and sentiment label (positive, negative, or neutral), forming a pioneering Arabic dataset aligned with healthcare themes, with ChatGPT-generated rationales provided for each label to enhance transparency. To evaluate the impact of annotation quality on model performance, we created three versions of the training data: a fully supervised set with all labels reviewed by humans, a semi-supervised set with 50% human review, and an unsupervised set with only machine-generated labels. We fine-tuned two transformer models on these datasets for both aspect and sentiment classification. Experimental results show that our Arabic-specific model achieved high accuracy even with minimal human supervision, reflecting only a minor performance drop when using ChatGPT-only labels. Reducing the number of aspect classes notably improved classification metrics across the board. These findings demonstrate an effective, scalable approach to Arabic aspect-based sentiment analysis (SA) in healthcare, combining large language model annotation with human expertise to produce a robust and explainable dataset. Future directions include generalisation across hospitals, prompt refinement, and interpretable data-driven modelling.
Speech Emotion Recognition (SER) is typically trained and evaluated on majority-voted labels, which simplifies benchmarking but masks subjectivity and provides little transparency into why predictions are made. This neglects valid minority annotations and limits interpretability. We propose an explainable Speech Language Model (SpeechLM) framework that frames SER as a generative reasoning task. Given an utterance, the model first produces a transcript, then outputs both an emotion label and a concise natural-language rationale grounded in lexical and acoustic cues. Rationales are generated by a reasoning-capable teacher LLM and used as intermediate supervision, combined with majority labels during fine-tuning. Unlike prior work primarily focused on boosting classification accuracy, we aim to enhance explainability while preserving competitive performance. To this end, we complement majority-label metrics with annotator-aware scoring that credits matches with any annotator label. On MSP-Podcast v1.12, our model maintains improvements over zero-shot SpeechLM baselines, and produces rationales that human evaluators find plausible and well grounded. This demonstrates that incorporating rationale supervision offers a practical path toward interpretable SER without sacrificing predictive quality.
A central challenge in explainable AI, particularly in the visual domain, is producing explanations grounded in human-understandable concepts. To tackle this, we introduce OCEAN (Object-Centric Explananda via Agent Negotiation), a novel, inherently interpretable framework built on object-centric representations and a transparent multi-agent reasoning process. The game-theoretic reasoning process drives agents to agree on coherent and discriminative evidence, resulting in a faithful and interpretable decision-making process. We train OCEAN end-to-end and benchmark it against standard visual classifiers and popular posthoc explanation tools like GradCAM and LIME across two diagnostic multi-object datasets. Our results demonstrate competitive performance with respect to state-of-the-art black-box models with a faithful reasoning process, which was reflected by our user study, where participants consistently rated OCEAN's explanations as more intuitive and trustworthy.
Electrospun yarns often fall short of the strength and stiffness of their constituent nanofibers because of loose packing and inter-fiber slip. We report a simple, twist-free route to close this gap by liquid-assisted rolling: yarns are briefly wetted (water or ethanol) and subjected to gentle rolling action (mechanical strokes perpendicular and parallel to the yarn axis), then dried under controlled conditions so that meniscus forces compact the assembly into tightly bound bundles. The treatment yields large gains in tensile strength and modulus, and as yarn diameter decreases the properties of liquid-treated yarns approach single-fiber limits, indicating more efficient load transfer. Dry-rolling controls produce negligible changes compared to as-spun yarns, confirming that capillarity-driven consolidation, rather than mechanical pressing, dominates the improvement. Water consistently outperforms ethanol, reflecting its larger elastocapillary driving term gamma*(1 + cos theta) on PAN and thus stronger capillary compaction; a short post-treatment anneal near Tg further increases stiffness with a corresponding reduction in ductility. To rationalize these trends, we quantify microstructure via SEM-derived alignment and packing density and show that these complementary descriptors jointly explain variability in mechanical response. A compact constitutive framework, grounded in distributed fiber recruitment and adhesion/frictional contact, captures the observed strengthening-ductility trade-off across processing routes. The results establish capillarity-driven consolidation as a scalable pathway to engineer processing-structure-property relationships in hierarchical polymer fiber assemblies and provide practical guidance for upgrading electrospun yarns, alone or as precursors to twisted and composite architectures.
Identifying long COVID symptoms is a challenging task, primarily due to the reliance on patient reports and the lack of disease specific biomarkers. The objective of this study is to identify individual long COVID symptoms, post COVID 19 conditions (PCC) participants, and participants' sex, and to identify the associated brain regions by developing an explainable machine learning algorithm using brain MRI features. This study implements secondary analysis using an anonymized, publicly accessible dataset that categorizes participants into three groups: the PCC group, the Unimpaired Post COVID 19 group (UPC), and the Healthy Non COVID group (HNC), each with corresponding symptoms, demographics, and brain structural MRI features. The aim is to develop and cross validate a support vector classifier (SVC) algorithm to identify the occurrence of various target labels from the dataset. The SVC classifier identified the occurrence of long-COVID symptoms with various performances for different target labels. The model performance and influential area are identified and discussed in light of previous research. The demonstrated approach offers an alternative modality for determining the occurrence of long COVID symptoms based on neuroimaging biomarkers.
Background Allergic contact dermatitis due to acrylates present in the workplace is a disease frequently reported among dentists, printers, and fiberglass workers. Recently, the number of cases of contact allergic dermatitis among beauticians specialized in sculpting artificial nails has increased. Objective Our objective was to study the clinical characteristics and allergens implicated in allergic contact dermatitis due to acrylates in beauticians and users of sculpted nails. Material and methods This was an observational, retrospective study of patients diagnosed with allergic contact dermatitis due to acrylates used in sculpting artificial nails over the last 26 years in the Hospital General Universitario, Valencia, Spain. Results In total, 15 patients were diagnosed: 14 beauticians and 1 client. Most cases were diagnosed in the past 2 years. All were women, their mean age was 32.2 years, and 26.7% had a personal or family history of atopy. The sensitization time varied between 1 month and 15 years. The most frequently affected areas were the fleshy parts of the fingers and hands. Three patients —2 beauticians and 1 client— presented allergic asthma due to acrylates. All patients underwent patch testing with a standard battery of allergens and a battery of acrylates. The most frequent allergens were ethylene glycol dimethacrylate (13/15, 86.7%), hydroxyethyl methacrylate (13/15, 86.7%), triethylene glycol dimethacrylate (7/15, 46.7%), 2-hydroxypropyl methacrylate (5/15, 33.3%), and methyl methacrylate (5/15, 33.3%). Conclusions Acrylate monomers used for sculpting artificial nails are important sensitizers for contact and occupational dermatitis. The most important consideration is primary and secondary prevention.
ABSTRACT According to the Nernst–Planck equation, the transport of charged species in porous electrodes is mainly driven by diffusion and migration. Although a number of all-vanadium redox flow battery (VRFB) models have been developed by several VRFB modeling groups, a comparative study of these two ion transport mechanisms has not been clearly reported in the literature. In this study, we develop a three-dimensional (3-D), transient VRFB model that rigorously accounts for both diffusion and migration mechanisms of charged species, including V2+, V3+, VO2+,VO2 + and H+. The VRFB model relies upon five principles of conservation: mass, momentum, species, electric charge, and thermal energy. Due to the general form of the conservation equations, both species migration effects on species transport and species diffusion effects on charge transport are considered in the source terms of the model equations. The model calculates species migration and diffusion fluxes through the membrane and compares their relative magnitudes under various charging and discharging stages. This paper clearly elucidates the role of species migration on vanadium crossover and the subsequent capacity losses, demonstrating that the present VRFB model is a valuable tool for optimizing the component design and operation of VRFBs.
Carbon-encapsulated nano-MnO composite with novel multiple structure loaded on N-doped carbon webs (CMNCWs) has been designed and fabricated by using polypyrrole webs as both template and precursor. As an anode material for lithium-ion batteries, CMNCWs exhibit a superhigh reversible capacity and excellent rate capability, delivering a capacity as high as 1268mAhg−1 after 700 cycles at a current density of 1.0Ag−1. Such superior electrochemical performance can be attributed to the unique multiple structure, which cannot only effectively shorten the transport path of Li+ ions and enhance the conductivity, but also relieve the volume change and prevent agglomeration of Mn grains during the phase transformation in the conversion reaction.
Search for simple and economical electrocatalyst for the hydrogen gas evolution reaction (HER), which can resemble to the performance of Pt and other precious metals, is a challenging research interest. In this work, a systematic effect of pre-treatment potential of screen-printed carbon (SPCE) surface on the HER performance in 0.5 M H2SO4 was carried out. A new observation of a low potential HER (onset potential, E onset = −0.02 V vs. RHE) at a cathodic potential, −0.5 V vs. Ag/AgCl on 1 hr pre-treated screen-printed carbon electrode (SPCE*, * = pre-treated) was observed. Physicochemical and electrochemical characterizations of the SPCE* by field emission scanning electron-microscope, Raman, IR and X-Ray photoelectron spectroscopes reveals specific generation of carboxylic acid functionalized carbon surface and in turn for the enhanced HER on the modified electrode surface. Electrochemical characterization of SPCE* with Fe(CN)6 3− support the observation. A marked decrement in the peak current and significant increase in the peak-to-peak separation potential response due electrostatic repulsion between the anion sites of Fe(CN)6 3− and –COO– were noticed. This observation is in parallel with the reduced electrical double layer capacitance value of the SPCE* system. The E onset and Tafel value (54.7 mV dec−1) obtained here are comparable to those at Pt, MoS2, MoSe2 and superior over the N- and P-doped graphene/carbon electrocatalysts for HER. A prototype HER system was developed and demonstrated for H2 gas production at a rate of 0.0053 μM s−1 (Operating potential = −0.5 V vs Ag/AgCl), which is comparable to that of precious metal and metal compound electrocatalysts based HER performance.