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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.