mstyslavity/prisma_v3 huggingface dataset:
Motivations: Explainable Artificial Intelligence (XAI) systems aim to improve users' understanding of AI, but XAI research shows many cases of different explanations serving some users well and being unhelpful to others. In non-AI systems, some software practitioners have used inclusive design approaches and sometimes their improvements turned out to be "curb-cut" improvements -- not only addressing the needs of underserved users, but also making the products better for everyone. So, if AI practitioners used inclusive design approaches, they too might create curb-cut improvements, i.e., better explanations for everyone. Objectives: To find out, we investigated the curb-cut effects of inclusivity-driven fixes on users' mental models of AI when using an XAI prototype. The prototype and fixes came from an AI team who had adopted an inclusive design approach (GenderMag) to improve their XAI prototype. Methods: We ran a between-subject study with 69 participants with no AI background. 34 participants used the original version of the XAI prototype and 35 used the version with the inclusivity fixes. We compared the two groups' mental model concepts scores, prediction accuracy, and inclusivity. Results: We found four main results. First, it revealed several curb-cut effects of the inclusivity fixes: overall increased engagement with explanations and better mental model concepts scores, which revealed fixes with curb-cut properties. However (second), the inclusivity fixes did not improve participants' prediction accuracy scores -- instead, it appears to have harmed them. This "curb-fence" effect (opposite of the curb-cut effect) revealed the AI explanations' double-edged impact. Third, the AI team's inclusivity fixes brought significant improvements for users whose problem-solving styles had previously been underserved. Further (fourth), the AI team's fixes reduced the gender gap by 45%.
Recently, the quality of artworks generated using Artificial Intelligence (AI) has increased significantly, resulting in growing difficulties in detecting synthetic artworks. However, limited studies have been conducted on identifying the authenticity of synthetic artworks and their source. This paper introduces AI-ArtBench, a dataset featuring 185,015 artistic images across 10 art styles. It includes 125,015 AI-generated images and 60,000 pieces of human-created artwork. This paper also outlines a method to accurately detect AI-generated images and trace them to their source model. This work proposes a novel Convolutional Neural Network model based on the ConvNeXt model called AttentionConvNeXt. AttentionConvNeXt was implemented and trained to differentiate between the source of the artwork and its style with an F1-Score of 0.869. The accuracy of attribution to the generative model reaches 0.999. To combine the scientific contributions arising from this study, a web-based application named ArtBrain was developed to enable both technical and non-technical users to interact with the model. Finally, this study presents the results of an Artistic Turing Test conducted with 50 participants. The findings reveal that humans could identify AI-generated images with an accuracy of approximately 58%, while the model itself achieved a significantly higher accuracy of around 99%.
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC by training neural models for induction (inferring latent functions) and transduction (directly predicting the test output for a given test input). We train on synthetically generated variations of Python programs that solve ARC training tasks. We find inductive and transductive models solve different kinds of test problems, despite having the same training problems and sharing the same neural architecture: Inductive program synthesis excels at precise computations, and at composing multiple concepts, while transduction succeeds on fuzzier perceptual concepts. Ensembling them approaches human-level performance on ARC.
This paper investigates the evolving landscape of decentralized finance (DeFi) by examining its foundational concepts, research trends, and ecosystem. A bibliometric analysis was conducted to identify thematic clusters and track the evolution of DeFi research. Additionally, a thematic review was performed to analyze the roles and interactions of key participants within the DeFi ecosystem, focusing on its opportunities and inherent risks. The bibliometric analysis identified a progression in research priorities, transitioning from an initial focus on technological innovation to addressing sustainability, environmental impacts, and regulatory challenges. Key thematic clusters include decentralization, smart contracts, tokenization, and sustainability concerns. The analysis of participants highlighted the roles of developers, liquidity providers, auditors, and regulators while identifying critical risks such as smart contract vulnerabilities, liquidity constraints, and regulatory uncertainties. The study underlines the transformative potential of DeFi to enhance financial inclusion and transparency while emphasizing the need for robust security frameworks and regulatory oversight to ensure long-term stability. This paper comprehensively explains the DeFi ecosystem by integrating bibliometric and thematic analyses. It offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on the sustainable development and integration of DeFi into the global financial system.
Understanding and predicting human migration patterns is a central challenge in population dynamics research. Traditional physics-inspired gravity and radiation models represent migration flows as functions of attractiveness using socio-economic features as proxies. They assume that the relationship between features and migration is spatially invariant, regardless of the origin and destination locations of migrants. We use Bayesian hierarchical models to demonstrate that migrant preferences likely vary based on geographical context, specifically the origin-destination pair. By applying these models to U.S. interstate migration data, we show that incorporating heterogeneity in a single latent migration parameter significantly improves the ability to explain variations in migrant flows. Accounting for such heterogeneity enables it to outperform classical methods and recent machine-learning approaches. A clustering analysis of spatially varying parameters reveals two distinct groups of migration paths. Individuals migrating along low-flow paths (typically between smaller populations or over larger distances) exhibit more nuanced decision-making. Their choices are less directly influenced by specific destination characteristics such as housing costs, land area, and climate-related disaster costs. High-flow path migrants appear to respond more directly to these destination attributes. Our results challenge assumptions of uniform preferences and underscore the value of capturing heterogeneity in migration models and policymaking.
Accurate diagnosis of gait impairments is often hindered by subjective or costly assessment methods, with current solutions requiring either expensive multi-camera equipment or relying on subjective clinical observation. There is a critical need for accessible, objective tools that can aid in gait assessment while preserving patient privacy. In this work, we present a mobile phone-based, privacy-preserving artificial intelligence (AI) system for classifying gait impairments and introduce a novel dataset of 743 videos capturing seven distinct gait patterns. The dataset consists of frontal and sagittal views of trained subjects simulating normal gait and six types of pathological gait (circumduction, Trendelenburg, antalgic, crouch, Parkinsonian, and vaulting), recorded using standard mobile phone cameras. Our system achieved 86.5% accuracy using combined frontal and sagittal views, with sagittal views generally outperforming frontal views except for specific gait patterns like Circumduction. Model feature importance analysis revealed that frequency-domain features and entropy measures were critical for classifcation performance, specifically lower limb keypoints proved most important for classification, aligning with clinical understanding of gait assessment. These findings demonstrate that mobile phone-based systems can effectively classify diverse gait patterns while preserving privacy through on-device processing. The high accuracy achieved using simulated gait data suggests their potential for rapid prototyping of gait analysis systems, though clinical validation with patient data remains necessary. This work represents a significant step toward accessible, objective gait assessment tools for clinical, community, and tele-rehabilitation settings
Calibration is a frequently invoked concept when useful label probability estimates are required on top of classification accuracy. A calibrated model is a function whose values correctly reflect underlying label probabilities. Calibration in itself however does not imply classification accuracy, nor human interpretable estimates, nor is it straightforward to verify calibration from finite data. There is a plethora of evaluation metrics (and loss functions) that each assess a specific aspect of a calibration model. In this work, we initiate an axiomatic study of the notion of calibration. We catalogue desirable properties of calibrated models as well as corresponding evaluation metrics and analyze their feasibility and correspondences. We complement this analysis with an empirical evaluation, comparing common calibration methods to employing a simple, interpretable decision tree.
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, struggles with the challenges posed by incomplete and noisy data within these graphs. In contrast, the rise of Neural Symbolic AI marks a significant advancement, merging the robustness of deep learning with the precision of symbolic reasoning. This integration aims to develop AI systems that are not only highly interpretable and explainable but also versatile, effectively bridging the gap between symbolic and neural methodologies. Additionally, the advent of large language models (LLMs) has opened new frontiers in knowledge graph reasoning, enabling the extraction and synthesis of knowledge in unprecedented ways. This survey offers a thorough review of knowledge graph reasoning, focusing on various query types and the classification of neural symbolic reasoning. Furthermore, it explores the innovative integration of knowledge graph reasoning with large language models, highlighting the potential for groundbreaking advancements. This comprehensive overview is designed to support researchers and practitioners across multiple fields, including data mining, AI, the Web, and social sciences, by providing a detailed understanding of the current landscape and future directions in knowledge graph reasoning.
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.
Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision support systems either provide no explainability of their predictions or use techniques like saliency and Shapley values, which do not allow for physician-based verification. To address this gap, this study compares previously used explainable AI techniques with a newly proposed technique termed '2-factor retrieval (2FR)', which is a combination of interface design and search retrieval that returns similarly labeled data without processing this data. This results in a 2-factor security blanket where: (a) correct images need to be retrieved by the AI; and (b) humans should associate the retrieved images with the current pathology under test. We find that when tested on chest X-ray diagnoses, 2FR leads to increases in clinician accuracy, with particular improvements when clinicians are radiologists and have low confidence in their decision. Our results highlight the importance of understanding how different modes of human-AI decision making may impact clinician accuracy in clinical decision support systems.
Stereotypes are generalised assumptions about societal groups, and even state-of-the-art LLMs using in-context learning struggle to identify them accurately. Due to the subjective nature of stereotypes, where what constitutes a stereotype can vary widely depending on cultural, social, and individual perspectives, robust explainability is crucial. Explainable models ensure that these nuanced judgments can be understood and validated by human users, promoting trust and accountability. We address these challenges by introducing HEARTS (Holistic Framework for Explainable, Sustainable, and Robust Text Stereotype Detection), a framework that enhances model performance, minimises carbon footprint, and provides transparent, interpretable explanations. We establish the Expanded Multi-Grain Stereotype Dataset (EMGSD), comprising 57,201 labelled texts across six groups, including under-represented demographics like LGBTQ+ and regional stereotypes. Ablation studies confirm that BERT models fine-tuned on EMGSD outperform those trained on individual components. We then analyse a fine-tuned, carbon-efficient ALBERT-V2 model using SHAP to generate token-level importance values, ensuring alignment with human understanding, and calculate explainability confidence scores by comparing SHAP and LIME outputs...
Neuronal heterogeneity, characterized by the presence of a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons. Despite the huge neuronal heterogeneity across brain regions, which in principle could decrease synchronization, cortical areas coherently oscillate during various cognitive tasks. Therefore, the functional significance of neuronal heterogeneity remains a subject of active investigation. Previous studies typically focus on the role of heterogeneity in the dynamic properties of only one population. Here, we explore how different types of inhibitory neurons can contribute to the diversity of the phase relations between two cortical areas. This research sheds light on the potential impact of local properties, such as neuronal variability, on communication between distant brain regions. We show that both homogeneous and heterogeneous inhibitory networks can exhibit phase diversity and nonintuitive regimes such as anticipated synchronization (AS) and phase bistability. It has been proposed that the bi-stable phase could be related to bi-stable perception, such as in the Necker cube. Moreover, we show that heterogeneity enlarges the region of zero-lag synchronization and bistability. We also show that the parameter controlling inhibitory heterogeneity modulates the transition from the usual delayed synchronization regime (DS) to AS. Finally, we show that the inhibitory heterogeneity drives the internal dynamics of the free-running population. Therefore, we suggest a possible mechanism to explain when the DS-AS transition occurs via zero-lag synchronization or bi-stability.
Evaluating off-policy decisions using batch data poses significant challenges due to limited sample sizes leading to high variance. To improve Off-Policy Evaluation (OPE), we must identify and address the sources of this variance. Recent research on Concept Bottleneck Models (CBMs) shows that using human-explainable concepts can improve predictions and provide better understanding. We propose incorporating concepts into OPE to reduce variance. Our work introduces a family of concept-based OPE estimators, proving that they remain unbiased and reduce variance when concepts are known and predefined. Since real-world applications often lack predefined concepts, we further develop an end-to-end algorithm to learn interpretable, concise, and diverse parameterized concepts optimized for variance reduction. Our experiments with synthetic and real-world datasets show that both known and learned concept-based estimators significantly improve OPE performance. Crucially, we show that, unlike other OPE methods, concept-based estimators are easily interpretable and allow for targeted interventions on specific concepts, further enhancing the quality of these estimators.
Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% ($r^2=96.4\%$) of the variance in value assessment can be explained by perceived risks ($\beta=-.504$) and perceived benefits ($\beta=+.710$), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.
This study proposes a qualitative analysis of self replies in Wikipedia talk pages, more precisely when the first two messages of a discussion are written by the same user. This specific pattern occurs in more than 10% of threads with two messages or more and can be explained by a number of reasons. After a first examination of the lexical specificities of second messages, we propose a seven categories typology and use it to annotate two reference samples (English and French) of 100 threads each. Finally, we analyse and compare the performance of human annotators (who reach a reasonable global efficiency) and instruction-tuned LLMs (which encounter important difficulties with several categories).
In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstanding results and are widely used in critical domains like autonomous driving and video surveillance. However, their adoption in high-risk applications, where errors may cause severe consequences, remains limited. Explainable Artificial Intelligence methods aim to address this issue, but many existing techniques are model-specific and designed for classification tasks, making them less effective for object detection and difficult for non-specialists to interpret. In this work we focus on model-agnostic explainability methods for object detection models and propose D-MFPP, an extension of the Morphological Fragmental Perturbation Pyramid (MFPP) technique based on segmentation-based masks to generate explanations. Additionally, we introduce D-Deletion, a novel metric combining faithfulness and localization, adapted specifically to meet the unique demands of object detectors. We evaluate these methods on real-world industrial and robotic datasets, examining the influence of parameters such as the number of masks, model size, and image resolution on the quality of explanations. Our experiments use single-stage object detection models applied to two safety-critical robotic environments: i) a shared human-robot workspace where safety is of paramount importance, and ii) an assembly area of battery kits, where safety is critical due to the potential for damage among high-risk components. Our findings evince that D-Deletion effectively gauges the performance of explanations when multiple elements of the same class appear in a scene, while D-MFPP provides a promising alternative to D-RISE when fewer masks are used.
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. However, the opacity of such black-box motion planners makes it challenging for the human behind the wheel to accurately anticipate when they will fail, with potentially catastrophic consequences. Here, we introduce concept-wrapper network (i.e., CW-Net), a method for explaining the behavior of black-box motion planners by grounding their reasoning in human-interpretable concepts. We deploy CW-Net on a real self-driving car and show that the resulting explanations refine the human driver's mental model of the car, allowing them to better predict its behavior and adjust their own behavior accordingly. Unlike previous work using toy domains or simulations, our study presents the first real-world demonstration of how to build authentic autonomous vehicles (AVs) that give interpretable, causally faithful explanations for their decisions, without sacrificing performance. We anticipate our method could be applied to other safety-critical systems with a human in the loop, such as autonomous drones and robotic surgeons. Overall, our study suggests a pathway to explainability for autonomous agents as a whole, which can help make them more transparent, their deployment safer, and their usage more ethical.