Research Papers

Deep learning has transformed computer vision (CV), achieving outstanding performance in classification, segmentation, and related tasks. Such AI-based CV systems are becoming prevalent, with applications spanning from medical imaging to surveillance. State of the art models such as convolutional neural networks (CNNs) and vision transformers (ViTs) are often regarded as ``black boxes,'' offering limited transparency into their decision-making processes. Despite a recent advancement in explainable AI (XAI), explainability remains underutilized in practical CV deployments. A primary obstacle is the absence of integrated software solutions that connect XAI techniques with robust knowledge management and monitoring frameworks. To close this gap, we have developed Obz AI, a comprehensive software ecosystem designed to facilitate state-of-the-art explainability and observability for vision AI systems. Obz AI provides a seamless integration pipeline, from a Python client library to a full-stack analytics dashboard. With Obz AI, a machine learning engineer can easily incorporate advanced XAI methodologies, extract and analyze features for outlier detection, and continuously monitor AI models in real time. By making the decision-making mechanisms of deep models interpretable, Obz AI promotes observability and responsible deployment of computer vision systems.

This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.

Achieving fairness in text-to-image generation demands mitigating social biases without compromising visual fidelity, a challenge critical to responsible AI. Current fairness evaluation procedures for text-to-image models rely on qualitative judgment or narrow comparisons, which limit the capacity to assess both fairness and utility in these models and prevent reproducible assessment of debiasing methods. Existing approaches typically employ ad-hoc, human-centered visual inspections that are both error-prone and difficult to replicate. We propose a method for evaluating fairness and utility in text-to-image models using Pareto-optimal frontiers across hyperparametrization of debiasing methods. Our method allows for comparison between distinct text-to-image models, outlining all configurations that optimize fairness for a given utility and vice-versa. To illustrate our evaluation method, we use Normalized Shannon Entropy and ClipScore for fairness and utility evaluation, respectively. We assess fairness and utility in Stable Diffusion, Fair Diffusion, SDXL, DeCoDi, and FLUX text-to-image models. Our method shows that most default hyperparameterizations of the text-to-image model are dominated solutions in the fairness-utility space, and it is straightforward to find better hyperparameters.

This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded and philosophically coherent analysis of regulatory blind spots. Our central claim is that what remains absent from policymaking is an account of the dynamic of becoming that underpins both the technical operation and economic logic of AI. To address this, we advance a formal reading of AI inspired by Simondonian philosophy of technology, reworking his concept of individuation to model the AI lifecycle, including the pre-individual milieu, individuation, and individuated AI. To translate these ideas, we introduce futurity: the self-reinforcing lifecycle of AI, where more data enhances performance, deepens personalisation, and expands application domains. Futurity highlights the recursively generative, non-rivalrous nature of data, underpinned by infrastructures like feature stores that enable feedback, adaptation, and temporal recursion. Our intervention foregrounds escalating power asymmetries, particularly the tech oligarchy whose infrastructures of capture, training, and deployment concentrate value and decision-making. We argue that effective regulation must address these infrastructural and temporal dynamics, and propose measures including lifecycle audits, temporal traceability, feedback accountability, recursion transparency, and a right to contest recursive reuse.

As algorithms increasingly take on critical roles in high-stakes areas such as credit scoring, housing, and employment, civil enforcement actions have emerged as a powerful tool for countering potential discrimination. These legal actions increasingly draw on algorithmic fairness research to inform questions such as how to define and detect algorithmic discrimination. However, current algorithmic fairness research, while theoretically rigorous, often fails to address the practical needs of legal investigations. We identify and analyze 15 civil enforcement actions in the United States including regulatory enforcement, class action litigation, and individual lawsuits to identify practical challenges in algorithmic discrimination cases that machine learning research can help address. Our analysis reveals five key research gaps within existing algorithmic bias research, presenting practical opportunities for more aligned research: 1) finding an equally accurate and less discriminatory algorithm, 2) cascading algorithmic bias, 3) quantifying disparate impact, 4) navigating information barriers, and 5) handling missing protected group information. We provide specific recommendations for developing tools and methodologies that can strengthen legal action against unfair algorithms.

Breakable Machine: A K-12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)

Olli Hilke, Nicolas Pope, Juho Kahila +4

This paper, submitted to the special track on resources for teaching AI in K-12, presents an eXplainable AI (XAI)-based classroom game "Breakable Machine" for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10-15, the game invites students to spoof an image classifier by manipulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models, this activity centers on breaking them-exposing their brittleness, bias, and vulnerability through hands-on, embodied experimentation. The game includes an XAI view to help students visualize feature saliency, revealing how models attend to specific visual cues. A shared classroom leaderboard fosters collaborative inquiry and comparison of strategies, turning the classroom into a site for collective sensemaking. This approach reframes AI education by treating model failure and misclassification not as problems to be debugged, but as pedagogically rich opportunities to interrogate AI as a sociotechnical system. In doing so, the game supports students in developing data agency, ethical awareness, and a critical stance toward AI systems increasingly embedded in everyday life. The game and its source code are freely available.

Objective: This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning. Approach: We examined a previously developed AI agent based on the Actor-Critic with Experience Replay (ACER) network, which automatically tunes treatment planning parameters (TPPs) for inverse planning in prostate cancer intensity modulated radiotherapy. We selected multiple checkpoint ACER agents from different stages of training and applied an explainable AI (EXAI) method to analyze the attribution from dose-volume histogram (DVH) inputs to TPP-tuning decisions. We then assessed each agent's planning efficacy and efficiency and evaluated their policy and final TPP tuning spaces. Combining these analyses, we systematically examined how ACER agents generated high-quality treatment plans in response to different DVH inputs. Results: Attribution analysis revealed that ACER agents progressively learned to identify dose-violation regions from DVH inputs and promote appropriate TPP-tuning actions to mitigate them. Organ-wise similarities between DVH attributions and dose-violation reductions ranged from 0.25 to 0.5 across tested agents. Agents with stronger attribution-violation similarity required fewer tuning steps (~12-13 vs. 22), exhibited a more concentrated TPP-tuning space with lower entropy (~0.3 vs. 0.6), converged on adjusting only a few TPPs, and showed smaller discrepancies between practical and theoretical tuning steps. Putting together, these findings indicate that high-performing ACER agents can effectively identify dose violations from DVH inputs and employ a global tuning strategy to achieve high-quality treatment planning, much like skilled human planners. Significance: Better interpretability of the agent's decision-making process may enhance clinician trust and inspire new strategies for automatic treatment planning.

As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions are threefold. First, we establish cognitive cybersecurity as a discipline complementing traditional cybersecurity and AI safety, addressing vulnerabilities where legitimate inputs corrupt reasoning while evading conventional controls. Second, we introduce the CIA+TA, extending traditional Confidentiality, Integrity, and Availability triad with Trust (epistemic validation) and Autonomy (human agency preservation), requirements unique to systems generating knowledge claims and mediating decisions. Third, we present a quantitative risk assessment methodology with empirically-derived coefficients, enabling organizations to measure cognitive security risks. We map our framework to OWASP LLM Top 10 and MITRE ATLAS, facilitating operational integration. Validation through previously published studies (151 human participants; 12,180 AI trials) reveals strong architecture dependence: identical defenses produce effects ranging from 96% reduction to 135% amplification of vulnerabilities. This necessitates pre-deployment Cognitive Penetration Testing as a governance requirement for trustworthy AI deployment.

The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, including 85% of respondents identifying ethical oversight as a key concern, and 72% emphasizing the need for localized governance structures. Building on these findings, we propose a draft Responsible AI (RAI) Framework tailored to resourceconstrained environments in these countries. Key elements of the framework include ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches to mitigate bias and ensure equitable healthcare outcomes.

Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely examination complicate detection. Artificial Intelligence (AI) models using retinal fundus photographs (RFPs) offer a promising solution. However, adoption in clinical settings is hindered by low-quality data and biases that may lead AI systems to learn unintended features. To address these challenges, we developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle. RAIS-DR integrates efficient convolutional models for preprocessing, quality assessment, and three specialized DR classification models. We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems. RAIS-DR demonstrated significant improvements, with F1 scores increasing by 5-12%, accuracy by 6-19%, and specificity by 10-20%. Additionally, fairness metrics such as Disparate Impact and Equal Opportunity Difference indicated equitable performance across demographic subgroups, underscoring RAIS-DR's potential to reduce healthcare disparities. These results highlight RAIS-DR as a robust and ethically aligned solution for DR screening in clinical settings. The code, weights of RAIS-DR are available at https://gitlab.com/inteligencia-gubernamental-jalisco/jalisco-retinopathy with RAIL.

Several Issues Regarding Data Governance in AGI

Masayuki Hatta

The rapid advancement of artificial intelligence has positioned data governance as a critical concern for responsible AI development. While frameworks exist for conventional AI systems, the potential emergence of Artificial General Intelligence (AGI) presents unprecedented governance challenges. This paper examines data governance challenges specific to AGI, defined as systems capable of recursive self-improvement or self-replication. We identify seven key issues that differentiate AGI governance from current approaches. First, AGI may autonomously determine what data to collect and how to use it, potentially circumventing existing consent mechanisms. Second, these systems may make data retention decisions based on internal optimization criteria rather than human-established principles. Third, AGI-to-AGI data sharing could occur at speeds and complexities beyond human oversight. Fourth, recursive self-improvement creates unique provenance tracking challenges, as systems evolve both themselves and how they process data. Fifth, ownership of data and insights generated through self-improvement raises complex intellectual property questions. Sixth, self-replicating AGI distributed across jurisdictions would create unprecedented challenges for enforcing data protection laws. Finally, governance frameworks established during early AGI development may quickly become obsolete as systems evolve. We conclude that effective AGI data governance requires built-in constraints, continuous monitoring mechanisms, dynamic governance structures, international coordination, and multi-stakeholder involvement. Without forward-looking governance approaches specifically designed for systems with autonomous data capabilities, we risk creating AGI whose relationship with data evolves in ways that undermine human values and interests.

As AI systems increasingly permeate everyday life, designers and developers face mounting pressure to balance innovation with ethical design choices. To date, the operationalisation of AI ethics has predominantly depended on frameworks that prescribe which ethical principles should be embedded within AI systems. However, the extent to which users value these principles remains largely unexplored in the existing literature. In a discrete choice experiment conducted in four countries, we quantify user preferences for 11 ethical principles. Our findings indicate that, while users generally prioritise privacy, justice & fairness, and transparency, their preferences exhibit significant variation based on culture and application context. Latent class analysis further revealed four distinct user cohorts, the largest of which is ethically disengaged and defers to regulatory oversight. Our findings offer (1) empirical evidence of uneven user prioritisation of AI ethics principles, (2) actionable guidance for operationalising ethics tailored to culture and context, (3) support for the development of robust regulatory mechanisms, and (4) a foundation for advancing a user-centred approach to AI ethics, motivated independently from abstract moral theory.