Artificial Intelligence Review in the AI ethics record
A source-linked view of 35 research records gathered from Artificial Intelligence Review. This page tracks what entered the ethics.ai source fleet; it is not a complete archive of the publisher and does not imply its endorsement.
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Latest records from Artificial Intelligence Review
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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