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The climate system is heavily influenced by aerosols through direct and indirect pathways, notably the absorption and scattering of radiant energy. Beyond climate impacts, these particles compromise atmospheric visibility and jeopardize public health. Specifically, human populations face exposure to fine particulate matter PM2.5, a major driver of respiratory and cardiovascular pathologies. Long-term modifications in PM levels are generally governed by emission shifts and meteorological patterns, whereas short-term fluctuations are predominantly dictated by immediate weather dynamics. Surface variations in PM2.5 levels are further driven by environmental variables such as temperature, wind velocity, relative humidity, and vegetative coverage.Air quality forecasting has advanced significantly through the application of machine learning. This research explores PM2.5 estimation by utilizing these environmental and meteorological metrics as spatiotemporal inputs. This study evaluates seven years (2014–2021) of in situ PM2.5 measurements gathered from five distinct monitoring stations across Singapore, establishing a baseline dataset for spatial modeling.The primary contribution of this paper is a novel predictive architecture tailored for temporal analysis, utilizing Random Forest (RF), Gradient Boosting (GB) regression, and the Tree-based Pipeline Optimization Tool (TP) Auto ML framework, which optimizes via a meta-heuristic genetic algorithm. The TP model demonstrated strong predictive capabilities, achieving a peak Global Performance Index (GPI) of 7.4 in August 2016 and a minimum value of −0.6 in June 2019. These metrics highlight the robust nature of the TP framework; its negative values remained superior to competing models, indicating less pessimistic errors. Finally, the underlying logic of these machine learning systems is clarified using eXplainable Artificial Intelligence (XAI) methods, which validate the integrity of feature importance and offer deeper insights into the cyclical variations of PM2.5 concentrations.

Although Reinforcement Learning (RL) exhibits significant potential for refining intricate control and decision-making systems, Deep Reinforcement Learning (DRL) suffers from a distinct lack of explainability. This limitation restricts its deployment within highly regulated domains, including manufacturing, finance, and healthcare. The opacity of DRL decision-making introduces structural challenges that impede operational efficiency and resource management—an issue that intensifies alongside ongoing algorithmic advancements. While current trends favor transitioning from traditional Experience Replay configurations to A3C architectures, the latter imposes substantially greater resource demands. Furthermore, despite continuous efforts to refine selection mechanisms within Experience Replay, repository capacities generally remain elevated. In this study, we evaluate a Deep Convolutional Q-learning agent across 20 Atari environments while deliberately downsizing the Experience Replay capacity from 1×106 to 5×102. Our findings indicate that lowering the capacity from 1×104 to 5×103 does not meaningfully degrade performance rewards, thereby establishing a viable strategy for resource-efficient DRL frameworks. To clarify agent choices and correlate them with inherent game mechanics, we introduce a novel methodology: visualizing the Experience Replay mechanism utilizing the Deep SHAP Explainer. This framework enhances domain comprehension and delivers transparent, interpretable accounts, though capacity constraints must be managed carefully to mitigate overfitting. Ultimately, this research validates the practicality of minimizing Experience Replay storage and supports the integration of transparent explanations via the Deep SHAP Explainer to advance resource conservation.

Machine learning (ML) techniques have been applied in diverse manners to combat the COVID-19 pandemic. Predictive ML models have been constructed, for example, for both diagnostic and prognostic objectives, utilizing varying data modalities such as textual, visual, and structured formats. Because of the distinct characteristics of this disease and its temporal transformation, a comprehensive understanding of the central elements driving the clinical trajectory of COVID-19 in individual patients remains incomplete. Throughout all phases of this study, a medical specialist was actively engaged via a human-in-the-loop framework. This collaborative element represents a vital yet frequently overlooked component of the ML and knowledge extraction (KE) workflow. Our findings indicate that explainable artificial intelligence (XAI) can offer substantial reinforcement to this dimension of ML and KE. This investigation centered on employing ML for knowledge extraction across two distinct operational scenarios. In the initial scenario, we sought to determine if integrating data regarding the dominant COVID-19 variant affects the predictive performance of the ML models. In the subsequent scenario, we concentrated on prognostic classification models designed to predict a patient's requirement for intensive care unit admission, integrating various XAI methodologies. We implemented nine distinct ML techniques, specifically XGBoost, CatBoost, LightGBM, logistic regression, Naive Bayes, random forest, SGD, SVM-linear, and SVM-RBF. The predictive quality of these models was evaluated using precision, accuracy, and AUC metrics. Following this evaluation, knowledge extraction was performed on the highest-performing models through two discrete pathways: (a) automated feature extraction using forward stepwise selection (FSS); and (b) the identification of attributes and their interactions using model explainability tools. Both sets of features were evaluated against a baseline configuration of attributes selected a priori by medical professionals using domain expertise. Our empirical tests demonstrated that incorporating data on the prevailing COVID-19 variant did not alter the performance of the generated ML models. Furthermore, the results indicated that human medical experts displayed significantly higher precision in identifying meaningful attributes compared to the automated FSS approach. Explainability tools identified nearly identical sets of attributes as the medical expert, while additionally uncovering noteworthy attribute interactions that the specialist subsequently evaluated from a clinical perspective. The outcomes of this investigation and their broader implications are examined.

The expansion of machine-learning frameworks into sectors with significant end-user consequences has heightened concerns regarding the equity of trained models, particularly the necessity of preventing biases that disadvantage distinct demographic sub-groups. The majority of current bias-mitigation techniques adjust the relative weight of data observations during the pre-processing phase. Because fairness depends heavily on context, we support an interactive machine-learning strategy that empowers human supervisors to offer iterative guidance for model refinement. To this end, we adapt the explanatory interactive machine-learning framework Caipi for equitable machine learning. This modified approach, FairCaipi, integrates human-in-the-loop interventions regarding both model predictions and underlying explanations to enhance model equity. Empirical outcomes indicate that FairCaipi outpaces a state-of-the-art pre-processing bias mitigation strategy in both the equity metrics and the predictive accuracy of the resulting machine-learning framework. We show that FairCaipi serves to expose and minimize algorithmic bias in machine-learning architectures while simultaneously identifying underlying human bias.

With the introduction of the Algorithmic Transparency Standard, UK authorities have renewed their emphasis on increasing operational openness for automated tools within public administration. This framework demonstrates that the UK is progressing its artificial intelligence regulatory goals while ensuring these benchmarks are grounded in the practical, real-world experiences of operators to achieve uniform, widespread integration. By utilizing a dual-level reporting structure, the Standard encourages inclusive disclosure tailored to different groups, thereby strengthening confidence among varied institutional and public actors. Furthermore, deploying these protocols across public agencies will likely serve as a blueprint for broader corporate benchmarks, guiding compliance models within commercial markets. This paper delivers a structured analysis and critique of the policy document.

Artificial intelligence increasingly informs consequential decisions, yet high predictive performance alone does not establish that an intelligent system is worthy of trust. This systematic review examines trustworthy AI as a multidimensional, socio-technical, and lifecycle-dependent research domain. It synthesises selected peer-reviewed journal evidence addressing transparency, interpretability, explainability, robustness, safety, privacy, security, fairness, accountability, governance, and meaningful human oversight. The review applies predefined questions, eligibility principles, trustworthiness-dimension mapping, claim-level evidence extraction, and cross-dimensional synthesis without undertaking meta-analysis or inventing quantitative screening results. The evidence indicates that transparency and explanation must be assessed in relation to fidelity, stability, completeness, usefulness, stakeholder needs, contestability, recourse, and the practical authority of human overseers. Robustness and safety require evaluation under adversarial conditions, uncertainty, context shift, system failure, and residual risk, while privacy-preserving approaches must still be examined for leakage, cybersecurity vulnerabilities, and governance limitations. Fairness depends on context-sensitive definitions, bias identification, distributional consequences, documentation, responsibility allocation, auditability, and institutional mechanisms for correction. Cross-dimensional analysis reveals persistent tensions between transparency and privacy, explainability and security, automation and human control, and performance and fairness. Important gaps remain in multidimensional evaluation, realistic human-oversight studies, explanation-usefulness validation, lifecycle monitoring, accountability operationalisation, stakeholder participation, and foundation-model assurance. The review contributes an integrated evidence synthesis showing that AI worthiness of trust should be supported through coordinated technical validation, human-centred assessment, institutional governance, and continuing monitoring rather than inferred from any isolated property.

Explainability has become central to discussions of trustworthy and responsible intelligent systems, yet it is frequently reduced to the technical production of information about model structure, features, rules, or predictions. This critical review examines why opening a computational black box is not equivalent to producing an explanation that people can understand or use responsibly. The review synthesises conceptual, technical, human-centred, decisional, and accountability-oriented scholarship to evaluate the shift from model interpretability toward meaningful explanation. It distinguishes technical properties such as transparency, fidelity, plausibility, completeness, stability, and local or global scope from human outcomes such as comprehension, cognitive accessibility, mental-model development, decision usefulness, calibrated trust, and appropriate reliance. It further examines how explanations may produce overreliance, underreliance, automation bias, false reassurance, cognitive overload, or explanation mismatch when their content and presentation are not aligned with users, tasks, uncertainty, and decision consequences. From an accountability perspective, explanations may contribute to auditability, traceability, contestability, and recourse, but they cannot independently assign responsibility, provide institutional authority, or guarantee effective remedies. The main contribution is a human-centred critical synthesis that treats explainability as a socio-technical achievement rather than a model output. Meaningful explanation requires technical credibility, cognitive accessibility, contextual relevance, decision-sensitive evaluation, uncertainty communication, opportunities for challenge, and governance mechanisms capable of acting on explanatory evidence. The review therefore rejects universal explanation-sufficiency claims and identifies conditions under which explanations may assist understanding and responsible decision use without being treated as proof of trustworthiness, accountability, fairness, safety, or deployment readiness.