{
  "count": 35,
  "items": [
    {
      "id": 19545,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11640-4",
      "title": "Reinforcement Learning and Model-based Planning in Practice: A Survey of Algorithmic Rationale and Domain Applications",
      "summary": "Reinforcement Learning (RL) is a foundational framework in Artificial Intelligence (AI) that enables agents to acquire optimal decision-making strategies through interactions with their environments. Building on principles of trial-and-error learning, RL adapts dynamically by leveraging feedback in the form of rewards or penalties. This paper provides a comprehensive survey of RL, its integration with Deep Learning into Deep Reinforcement Learning (DRL), and the emerging field of model-based pla",
      "authors": null,
      "category": "research",
      "topics": "agents-autonomy,environment",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/19545"
    },
    {
      "id": 19546,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11648-w",
      "title": "Data augmentation in multimodal frameworks: a survey",
      "summary": "Training machine learning models with more than one data modality has enhanced predictive performance in most contexts. Thus, many recent applications of machine learning use data from different sources and forms. Multimodal data augmentation (MMDA) addresses critical challenges in multimodal learning, such as data scarcity, modality imbalance, and cross-modal alignment. This survey systematically reviews 68 state-of-the-art MMDA approaches, and, as result, proposes a taxonomy for the area. For",
      "authors": null,
      "category": "research",
      "topics": "safety-alignment",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/19546"
    },
    {
      "id": 17855,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11674-8",
      "title": "A survey of deep multivariate time-series models with an empirical reproducibility audit",
      "summary": "Multivariate time series (MTS) analysis is increasingly important for extracting insights from complex, interdependent temporal data in domains such as healthcare, finance, and industrial monitoring. Recent advances in deep learning have significantly improved MTS modeling; yet, the rapidly expanding literature remains fragmented across tasks, architectures, and evaluation practices. This survey concentrates on deep learning-centric approaches in MTS research across seven key tasks: classificati",
      "authors": null,
      "category": "research",
      "topics": "healthcare,transparency",
      "published_at": "2026-08-09T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/17855"
    },
    {
      "id": 17705,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11659-7",
      "title": "Deep learning in precision phytopathology: a comprehensive survey of CNN architectures for disease detection and severity quantification",
      "summary": "Plant disease detection and severity estimation are crucial to sustainable agricultural productivity and global food security, necessitating the need for efficient and accurate diagnostic tools. This paper systematically analyzes 137 studies using the PRISMA 2020 framework, focusing on deep learning methods used in detecting and estimating plant disease severity. The review covers classification, detection, segmentation, and regression approaches to diagnosing plant diseases and quantifying seve",
      "authors": null,
      "category": "research",
      "topics": "jobs-economy,healthcare",
      "published_at": "2026-08-08T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/17705"
    },
    {
      "id": 17492,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11666-8",
      "title": "Epistemic norms for AI safety and alignment research",
      "summary": "Mainstream AI research emphasises capability growth and tolerates low failure rates when average-case performance is high. AI safety and alignment research has a different mission: to ensure that catastrophic failures never occur, under sparse evidence, adversarial dynamics, and fat-tailed risk. We argue that the two domains differ along two analytically independent axes — capability profile (demonstrating the absence of hazardous behaviours versus the presence of positive capabilities) and risk",
      "authors": null,
      "category": "research",
      "topics": "safety-alignment",
      "published_at": "2026-08-07T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/17492"
    },
    {
      "id": 15590,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11662-y",
      "title": "A comprehensive review of benchmark datasets for deep learning-based medical image segmentation",
      "summary": "This paper undertakes a systematic investigation of the medical image segmentation benchmark datasets, which play a crucial role in the notable progress of medical image segmentation task. The datasets serve as the foundational infrastructure comparable to a backbone that supports and drives the development of medical image segmentation. Consequently, examination of these datasets emerges as a critical topic in research. In order to address the current lack of a systematic summary and thorough a",
      "authors": null,
      "category": "research",
      "topics": "healthcare,finance-investment",
      "published_at": "2026-08-01T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/15590"
    },
    {
      "id": 15399,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11655-x",
      "title": "HyperTransUrban: a vision transformer-driven survey of change detection using hyperspectral imaging",
      "summary": "In the twenty-first century, urbanization has become one of the most transformative processes, driving significant changes in land use, infrastructure, and environmental conditions. These dynamics underscore the need for accurate urban planning, environmental monitoring, and disaster management. Traditional approaches utilize optical and multispectral remote sensing imagery and often fail to distinguish spectrally similar urban materials. In contrast, hyperspectral imaging improves classificatio",
      "authors": null,
      "category": "research",
      "topics": "environment",
      "published_at": "2026-07-31T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/15399"
    },
    {
      "id": 15400,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11658-8",
      "title": "From 2D image synthesis to 3D scene generation: a comprehensive review of synthetic data for agricultural vision",
      "summary": "Despite rapid digitalization, agricultural computer vision still faces persistent data bottlenecks. The collection and annotation of field images are constrained by seasonality and biological variability, require domain expertise, must adapt to the perception and navigation conditions of ground robots, and are further limited by privacy and data-sharing concerns. Together, these factors restrict the scale, diversity, and transferability of real-world datasets. This paper provides a systematic re",
      "authors": null,
      "category": "research",
      "topics": "privacy-surveillance,agents-autonomy,biotech",
      "published_at": "2026-07-31T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/15400"
    },
    {
      "id": 15401,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11653-z",
      "title": "Deep learning for security-relevant event detection in visual data: a structured narrative review of the state of the art and future challenges",
      "summary": "Deep learning has become a key enabling technology for detecting security-relevant events in visual surveillance data acquired from CCTV systems, UAV platforms, and other imaging sensors. However, despite substantial progress in benchmark performance, the operational deployment of such systems remains challenging due to dataset bias, domain shift, limited robustness, edge-computing constraints, and a lack of operationally meaningful evaluation metrics. This structured narrative review synthesise",
      "authors": null,
      "category": "research",
      "topics": "bias-fairness,privacy-surveillance",
      "published_at": "2026-07-31T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/15401"
    },
    {
      "id": 15001,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11650-2",
      "title": "Automated machine learning in the era of large language models: a systematic review of green, trustworthy, and human-centered automation (2020–2026)",
      "summary": "Automated Machine Learning (AutoML) has rapidly transformed the landscape of artificial intelligence by democratizing access to sophisticated machine learning models and streamlining complex development workflows. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, comprehensively analyzes the evolution of AutoML from 2020 to early 2026 (final search conducted in early February 2026), with a particular focus on the integration of Large Language Models (LLMs) and the",
      "authors": null,
      "category": "research",
      "topics": "jobs-economy",
      "published_at": "2026-07-30T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/15001"
    },
    {
      "id": 13866,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11642-2",
      "title": "Achieving the quantum advantage across smart grid: delineating challenges, opportunities, and future crosswalks",
      "summary": "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",
      "authors": null,
      "category": "research",
      "topics": "environment",
      "published_at": "2026-07-28T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/13866"
    },
    {
      "id": 13176,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11654-y",
      "title": "AI-driven multi-tier aerial communication networks: a review of routing, computing, handover, resource management, and optimization techniques",
      "summary": "Multi-tier aerial communication networks (MACNs), integrating satellites, high-altitude platforms, and unmanned aerial vehicles, are emerging as a cornerstone of next-generation global connectivity. Their promise of resilient and ubiquitous coverage, however, is hindered by highly dynamic topologies, severe energy and computational constraints, environment-sensitive channels, diverse quality-of-service requirements, and limited real-world validation. Artificial intelligence (AI) has increasingly",
      "authors": null,
      "category": "research",
      "topics": "environment",
      "published_at": "2026-07-23T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/13176"
    },
    {
      "id": 12733,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11649-9",
      "title": "Large language models and multimodal AI for mental health: a systematic review of early diagnosis and monitoring",
      "summary": "Mental health disorders (e.g., depression, anxiety, post-traumatic stress disorder (PTSD), bipolar disorder) represent a pressing global challenge, and early diagnosis with continuous monitoring is critical for effective intervention. However, traditional diagnostic methods, relying on patient self-reports and clinical interviews, are subjective and often miss subtle early warning signs, a problem compounded by stigma and limited access to care. In response, recent advances in artificial intelli",
      "authors": null,
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-22T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/12733"
    },
    {
      "id": 12038,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11635-1",
      "title": "Brain-inspired artificial intelligence for self-healing microgrids: a comprehensive review",
      "summary": "The rapid integration of renewable energy sources and the decentralization of power systems have positioned microgrids as essential for sustainable, resilient energy supply. However, their diverse operating conditions and complex topologies pose challenges for stability, protection, and autonomous control, particularly under fault conditions. This article surveys brain-inspired artificial intelligence (BIAI) models that enable self-healing functions in Microgrids (MGs). It covers structure-drive",
      "authors": null,
      "category": "research",
      "topics": "environment",
      "published_at": "2026-07-20T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/12038"
    },
    {
      "id": 11795,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11644-0",
      "title": "The value of contact in legged locomotion: a survey of sensing channels, artificial intelligence and control",
      "summary": "Legged robots traverse unstructured terrain through brief, intermittent foot–ground contacts whose support conditions are difficult to perceive and predict in real time. In such regimes, haptic feedback provides early and trustworthy evidence of traction limits, partial support, and incipient slip. This structured survey asks two questions: first, what locomotion-relevant contact evidence can be acquired and preserved under real deployment constraints; and second, how that evidence is translated",
      "authors": null,
      "category": "research",
      "topics": "agents-autonomy",
      "published_at": "2026-07-19T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/11795"
    },
    {
      "id": 11064,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11643-1",
      "title": "Exploring generative AI through core frameworks, emerging innovations, and applications",
      "summary": "Generative Artificial Intelligence has undergone rapid maturation between 2023 and 2025, driven by three converging paradigm shifts: the emergence of multimodal foundation models unifying text, image, audio, and video synthesis; the rise of agentic autonomy transforming generative systems into goal-driven, autonomous entities; and the formalization of responsible AI governance through legally enforceable regulations. While this technological landscape has generated substantial economic impact, c",
      "authors": null,
      "category": "research",
      "topics": "regulation,agents-autonomy",
      "published_at": "2026-07-16T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/11064"
    },
    {
      "id": 10682,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11641-3",
      "title": "All too perfect: bias and aspiration in persona generation with LLMs",
      "summary": "Synthetic data generated by large language models plays a central role in the training and alignment process of other AI systems. However, this process also risks inheriting the structural biases of organic corpora and embedding new biases that stem from the design choices underlying the data creation process. This paper examines the systematic biases that emerge when large language models (LLMs) are tasked with generating synthetic personas. We introduce a reproducible, minimally conditioned pi",
      "authors": null,
      "category": "research",
      "topics": "bias-fairness,safety-alignment",
      "published_at": "2026-07-15T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/10682"
    },
    {
      "id": 10683,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11645-z",
      "title": "Towards transparent financial AI: a systematic review of graph learning and explainable methods for credit risk and fraud detection",
      "summary": "Graph-based learning and explainable artificial intelligence (XAI) are increasingly used to improve both predictive performance and transparency in financial risk modelling. This paper presents a systematic literature review of AI and machine learning approaches for credit risk assessment and fraud detection, with specific attention to graph-based methods and explainable frameworks. Following a PRISMA-guided methodology, 149 studies published between 2015 and 2025 were analysed across multiple a",
      "authors": null,
      "category": "research",
      "topics": "transparency",
      "published_at": "2026-07-15T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/10683"
    },
    {
      "id": 1978,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11612-8",
      "title": "A systematic survey of blockchain-enabled artificial intelligence for industrial IoT: recent advances, integration challenges, and future prospects",
      "summary": "In the rapidly evolving landscape of technology, Blockchain (BC), Artificial Intelligence (AI), and Smart Industrial Internet of Things (IIoT) are leading and promising technologies in the world that facilitate the current society to develop the quality of living and make it simpler for users. However, these technologies have been applied in various domains for different purposes. These technologies successfully assist in developing the desired system, such as smart cities, homes, education, and",
      "authors": null,
      "category": "research",
      "topics": "children-education",
      "published_at": "2026-07-11T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1978"
    },
    {
      "id": 1979,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11627-1",
      "title": "Balancing fairness and influence spread in social networks: a multi-objective evolutionary approach",
      "summary": "Influence maximization in social networks has received increasing attention, particularly in applications where fairness among demographic groups is an important concern. However, many existing approaches either overlook group-level disparities or primarily optimize influence spread without explicitly modeling fairness-related trade-offs. In this paper, we propose a group-aware multi-objective evolutionary framework that decomposes seed sets into group-specific sub-solutions. Each demographic gr",
      "authors": null,
      "category": "research",
      "topics": "bias-fairness",
      "published_at": "2026-07-11T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1979"
    },
    {
      "id": 1980,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11646-y",
      "title": "AI-governed hospitals-of-the-future under industry 5.0: intelligent personalisation, cloud-integrated AI, and human-centred governance",
      "summary": "Artificial intelligence is reshaping hospital care delivery through federated learning pipelines, edge-cloud inference architectures, and AI-driven clinical decision support. Yet the translation of these AI capabilities into patient-centred, institutionally governable, and humanised hospital systems remains fragmented across the literature. This paper addresses that gap through a PRISMA-compliant systematic evidence synthesis of 116 included studies and reports (inter-rater reliability $$\\kappa ",
      "authors": null,
      "category": "research",
      "topics": "regulation,healthcare",
      "published_at": "2026-07-11T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1980"
    },
    {
      "id": 1981,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11630-6",
      "title": "Social cognitive architecture for NPC groups: integration of transformer theory of mind and hierarchical reinforcement learning",
      "summary": "Non-Player Characters (NPCs) require social cognition to enable intelligent and interactive behaviours within virtual environments. In gaming and other multi-agent systems, current NPC models often fall short in social intelligence and coordination because they cannot infer or anticipate the mental states of other agents. To address this gap, this paper introduces a novel social cognitive architecture that integrates Hierarchical Reinforcement Learning (HRL) with a Transformer-based Theory of Mi",
      "authors": null,
      "category": "research",
      "topics": "agents-autonomy,environment",
      "published_at": "2026-07-07T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1981"
    },
    {
      "id": 1982,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11634-2",
      "title": "The interpretability paradox in cancer imaging and risk prediction: a critical narrative review of explainable AI, failure modes, and design alternatives",
      "summary": "Deep learning has advanced cancer imaging and cancer-related risk prediction, but many high-performing models remain difficult to interrogate in clinically meaningful terms. This creates an interpretability paradox: gains in predictive performance often coincide with reduced transparency, while widely used post-hoc explanations can be persuasive without providing reliable evidence of model reasoning. Here, we present a critical narrative review and position argument, supported by a semi-systemat",
      "authors": null,
      "category": "research",
      "topics": "safety-alignment,transparency",
      "published_at": "2026-07-07T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1982"
    },
    {
      "id": 1983,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11631-5",
      "title": "Symmetry-aware learning in machine intelligence: architectural principles and deployment trade-offs",
      "summary": "Symmetry-aware machine learning (ML) embeds invariance and equivariance constraints directly into model architectures, providing principled inductive biases that can improve generalization, sample efficiency, and alignment with data-generating processes. This survey presents a principled synthesis of symmetry-aware architectures, treating symmetry as the core architectural design axis, rather than as an auxiliary modeling property. We introduce a structured taxonomy of symmetry types by mapping ",
      "authors": null,
      "category": "research",
      "topics": "safety-alignment",
      "published_at": "2026-07-05T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1983"
    },
    {
      "id": 1984,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11633-3",
      "title": "Deep learning for heart disease anomaly detection: performance factors and algorithms",
      "summary": "Heart disease is a prevalent concern for individuals in every age group, as it significantly impacts their health and remains a leading cause of mortality today. An effective heart disease detection method is essential for people to assess their heart conditions accurately. Over the past decades, heart disease detection techniques, whether based on machine learning or deep learning, have evolved considerably—from relying on handcrafted features to automatically learned features, from using singl",
      "authors": null,
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-05T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1984"
    },
    {
      "id": 1985,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11632-4",
      "title": "Fairness in federated medical imaging: a systematic review through the dual fairness lens",
      "summary": "Federated learning (FL) enables multi-institutional collaboration in medical imaging while preserving patient privacy, yet its fairness landscape remains fragmented: existing methods predominantly address either collaboration fairness (equitable performance across institutions) or group fairness (equitable outcomes across demographic subgroups), but rarely both. In this systematic review, we adopt dual fairness —the joint satisfaction of both dimensions—as the analytical lens for organizing and ",
      "authors": null,
      "category": "research",
      "topics": "bias-fairness,privacy-surveillance,healthcare",
      "published_at": "2026-07-05T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1985"
    },
    {
      "id": 1986,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11600-y",
      "title": "Transformers for 3D medical image analysis: a systematic review of architectural innovations, performance, and clinical applications",
      "summary": "The growing integration of Transformer-based architectures into 3D medical image analysis has driven significant advances across segmentation, classification, detection, registration, and reconstruction tasks. However, existing reviews remain fragmented, often focusing on 2D medical image analysis or specific modalities or tasks without providing a comprehensive, structured synthesis of architectural innovations, benchmark performance, and clinical applicability. This systematic review addresses",
      "authors": null,
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-05T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1986"
    },
    {
      "id": 1987,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11636-0",
      "title": "Mitigating cyberattacks on autonomous vehicles: a comprehensive review of Generative Artificial Intelligence defense techniques",
      "summary": "Autonomous vehicles (AVs) are rapidly becoming foundational components of intelligent transportation systems (ITS), yet their complex cyber-physical architectures expose them to a broad and continuously evolving threat landscape. Existing cybersecurity solutions struggle to keep pace with the dynamic, data-intensive nature of AV ecosystems, leaving critical vulnerabilities unaddressed across perception, communication, and decision-making subsystems. Generative Artificial Intelligence (GAI), enco",
      "authors": null,
      "category": "research",
      "topics": "military-security",
      "published_at": "2026-07-05T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1987"
    },
    {
      "id": 1988,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11625-3",
      "title": "A comprehensive review of recent advancements in hyperspectral object tracking",
      "summary": "Visual object tracking is a fundamental problem in computer vision. Traditional tracking methods, which primarily rely on RGB imagery, often face difficulties in complex scenarios such as low resolution and background clutter. Hyperspectral imaging, which captures both spatial and spectral information across multiple narrow spectral bands, has emerged as a promising solution. However, hyperspectral tracking suffers from challenges including the complexity of spatial-spectral-temporal modeling, t",
      "authors": null,
      "category": "research",
      "topics": "privacy-surveillance",
      "published_at": "2026-07-04T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1988"
    },
    {
      "id": 1989,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11595-6",
      "title": "A review on empirical studies in explainable artificial intelligence",
      "summary": "As artificial intelligence (AI) systems become more integrated into decision-making processes, the need for explainability has emerged to foster trust, understanding, and effective human-AI collaboration. With the variety of explainable AI (XAI) methods available, selecting the right one for a specific user group and a given use case remains challenging, especially given the limited empirical validation of existing theoretical guidance. This systematic literature review addresses this gap by syn",
      "authors": null,
      "category": "research",
      "topics": "transparency",
      "published_at": "2026-07-03T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1989"
    },
    {
      "id": 1990,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11548-z",
      "title": "A systematic review of toxicity in large language models: definitions, datasets, detectors, detoxification methods and challenges",
      "summary": "The emergence of the transformer architecture has ushered in a new era of possibilities, showcasing remarkable capabilities in generative tasks exemplified by models like GPT4o, Claude 3, and Llama 3. However, these advancements come with a caveat: predominantly trained on data gleaned from social media platforms, these systems inadvertently perpetuate societal biases and toxicity. Recognizing the paramount importance of AI Safety and Alignment, our study embarks on a thorough exploration throug",
      "authors": null,
      "category": "research",
      "topics": "safety-alignment",
      "published_at": "2026-07-02T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1990"
    },
    {
      "id": 1991,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11638-y",
      "title": "Engineering carbon credits with AI towards a responsible FinTech era: the practices, implications, and future",
      "summary": "Carbon emissions drive climate change, and carbon credits mitigate climate deterioration and environmental damage while assisting organizations in managing their carbon footprint. Fully utilizing carbon credits remains challenging. This study enhances understanding of the engineering practices for carbon credits to develop responsible fintech solutions and provide insights for carbon emission management. We review the negative impacts of organizations’ strategy of evading carbon management throu",
      "authors": null,
      "category": "research",
      "topics": "environment,finance-investment",
      "published_at": "2026-07-02T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1991"
    },
    {
      "id": 1992,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11626-2",
      "title": "A systematic review of multisensor methods for open-carry and concealed knife detection",
      "summary": "Reliable detection of openly carried and concealed knives remains a challenging problem in artificial intelligence (AI) due to the small size, thin geometry, frequent occlusion, and material variability of blade objects. Although advances in deep learning have improved weapon detection performance, the literature remains fragmented across sensing modalities, datasets, and evaluation protocols, limiting reproducibility and systematic comparison. This paper presents a systematic and modality-aware",
      "authors": null,
      "category": "research",
      "topics": "military-security",
      "published_at": "2026-07-01T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1992"
    },
    {
      "id": 1993,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11587-6",
      "title": "Supervised machine learning classifiers for schizophrenia and bipolar disorder using speech and language: a systematic review, meta-analysis, and novel quality assessment framework",
      "summary": "This paper presents a systematic review and meta-analysis of 62 studies that developed speech- and language-based AI for severe mental illnesses (SMI) (i.e., characterized by substantial communication problems affecting speech production and language). We employed a random-effects meta-analysis using Restricted Maximum Likelihood (REML). We evaluated these studies using our proposed rigorous 16-item quality assessment framework, grouped into three domains: Study Design, Fairness and Explainabili",
      "authors": null,
      "category": "research",
      "topics": "bias-fairness,transparency",
      "published_at": "2026-06-29T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1993"
    },
    {
      "id": 1994,
      "url": "https://link.springer.com/article/10.1007/s10462-026-11603-9",
      "title": "Recent advances in AI-based mobile robots for human companionship: survey",
      "summary": "Human companionship is an essential capability for mobile robots operating in dynamic, human-centered environments. It enables robots to perform tasks such as guidance, assistance, surveillance, and service delivery across various domains, including healthcare, logistics, and public safety. The recent advances in artificial intelligence (AI), particularly in computer vision, deep learning, and sensor fusion, have significantly improved the reliability, adaptability, and contextual understanding ",
      "authors": null,
      "category": "research",
      "topics": "privacy-surveillance,healthcare,agents-autonomy,environment",
      "published_at": "2026-06-29T00:00:00.000Z",
      "source": "Artificial Intelligence Review",
      "ethics_ai_record_url": "https://ethics.ai/record/1994"
    }
  ],
  "attribution": "via ethics.ai"
}