Explainable Artificial Intelligence in Human Resources: a Computational Study

Over the past few years, machine learning and AI have become increasingly common in human resources (HR) applications, such as candidate screening, resume parsing, and employee attrition and turnover prediction. Though AI assists in making these tasks more efficient, and seemingly less biased through automation, it relies heavily on data created by humans, and consequently can have human biases carry over to decisions made by a model. Several studies have shown biases in machine learning applications such as facial recognition and candidate ranking. This has spurred active research on the topic of fairness in machine learning over the last five years. Several toolkits to mitigate biases and interpret black box models have been developed in an effort to promote fair algorithms. This paper presents an overview of fairness definitions, methods, and tools as they relate to recruitment and establishes ethical considerations in the use of machine learning in the hiring space.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

The oil and gas industry faces constant pressure to enhance operational efficiency, reduce costs, and minimize equipment downtime. Machine learning (ML) applications in predictive maintenance have emerged as a transformative approach to achieving these goals. This review explores the role of machine learning in predictive maintenance, highlighting its potential to revolutionize maintenance strategies and improve asset management across the industry. Predictive maintenance leverages advanced algorithms to analyze historical and real-time data from equipment and sensors, enabling the identification of patterns and anomalies that precede equipment failures. By utilizing techniques such as supervised learning, unsupervised learning, and reinforcement learning, organizations can forecast equipment malfunctions and schedule maintenance activities proactively, thereby reducing unexpected downtimes and extending asset lifecycles. The implementation of ML in predictive maintenance provides several key benefits. Firstly, it enhances operational efficiency by optimizing maintenance schedules and minimizing unplanned outages, which are critical in the capital-intensive oil and gas sector. Secondly, it enables cost savings through more efficient resource allocation and reduced labor costs associated with reactive maintenance strategies. Thirdly, machine learning algorithms can continuously learn from new data, refining their predictive capabilities and improving accuracy over time. Several case studies illustrate the successful application of machine learning in predictive maintenance within the oil and gas industry. For example, ML models have been employed to predict pump failures, optimize drilling operations, and improve pipeline integrity monitoring. These applications not only lead to significant financial savings but also enhance safety by reducing the risk of catastrophic failures. In conclusion, machine learning applications in predictive maintenance represent a crucial advancement for the oil and gas industry. By harnessing the power of data-driven insights, organizations can enhance operational efficiency, reduce costs, and ultimately drive sustainable growth. This review emphasizes the transformative potential of machine learning in predictive maintenance, establishing it as a key strategy for success in the ever-evolving oil and gas landscape.

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Employee attrition is a reduction in employees that happens gradually. Employee attrition can damage the organization of a company, including the projects and its employee structure. This study aims to predict employee attrition in a company using the logistic regression method. Employee attrition can be predicted using machine learning because the machine learning approach is not biased due to human interference. In addition, human resources in a company need to know the most influential factors that cause the occurrence of employee attrition. In this study, we proposed feature selection methods to identify those influential factors and simplify the data training. Our approach is to predict employee attrition with three kinds of feature selection methods, namely information gain, select k-best, and recursive feature elimination (RFE). The 10-fold cross-validation was performed as an evaluation method. Prediction of employee attrition using the logistic regression method without applying feature selection gets an accuracy value of 0.865 and an AUC score of 0.932. However, by applying the RFE feature selection showed the highest evaluation result than information gain and select k-best, with an accuracy value of 0.853 and an AUC score of 0.925

Purpose Although there are studies analyzing barriers related to new technological concepts, it turns out that there are only a few studies on barriers to machine learning (ML) applications, and none of them consider the implications for smart logistics. Therefore, the purpose of this study is to reveal and analyze the barriers to ML applications in smart logistics from both industry and academic perspectives. Design/methodology/approach To achieve this aim, first, various barriers to smart logistics activities based on the Industry 4.0 perspective are identified. Later, the relative importance of these barriers critical to the success of smart logistics activities is determined. Finally, the interval-valued fuzzy (IVF) DEMATEL method is used to analyze the cause-and-effect relationship between each barrier based on industry and academic perspective. Findings Eleven barriers related to ML applications in smart logistics were evaluated by seven experts who are working in different positions. Results show that, the most crucial cause-and-effect barriers are integration and connection problems with value chain/network systems (B6), requirements of adapting new infrastructures (B11) and lack of transparency, safety and security (B3). Originality/value There is no study about determining barriers with merging smart logistics activities with the Industry 4.0 perspective. It is expected that the results of this study will contribute to the use of ML in the logistics sector by revealing significant concepts to which businesses should pay attention to prevent these barriers and by suggesting practical solutions to these problems.

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

A cost effective, easily scalable, and application independent FPGA cluster co-processing platform for machine learning (ML) applications is proposed. The work in this paper focuses on delivering an economical platform for the researchers and developers without the knowledge of FPGAs who seek budget friendly solutions to increase the performance of their ML applications without relying on the highly expensive solutions available in the today's FPGA market. The approach consists of two main parts, with a CPU based host machine learning application and the FPGA cluster sharing the heavy workload through an Ethernet protocol. An experiment was conducted using a perceptron layer implementation on the hardware and the test results show the execution time improvements due to the utilization of the proposed cluster approach. The solution proposed is easily scalable with any FPGA platform with an Ethernet support.

Employee turnover remains a critical and costly challenge for organizations globally, necessitating advanced predictive tools beyond traditional statistical methods. This systematic literature review (SLR) investigates the application of machine learning (ML) models in predicting employee attrition, synthesizing findings from 42 peer-reviewed articles and conference proceedings published between January 2020 and September 2025. Following the PRISMA 2020 guidelines, the review analyzes the algorithmic landscape, predictive feature importance, and methodological challenges in this domain. Results indicate that ensemble methods, particularly Random Forest and XGBoost, consistently outperform baseline models such as Logistic Regression, achieving typical accuracies between 85% and 94%. A consistent set of features-including low job satisfaction, excessive overtime workload, and below-market compensation-emerged as the strongest predictors of attrition risk. Furthermore, the review highlights the critical role of Explainable AI (XAI) techniques, such as SHAP and LIME, in translating complex model predictions into actionable insights for Human Resources (HR) professionals, thereby addressing concerns about model opacity and fostering stakeholder trust. Methodological challenges, including class imbalance and the risk of algorithmic bias, are discussed alongside common mitigation strategies. This review concludes by outlining emerging trends, such as hybrid models and prescriptive analytics, providing a comprehensive reference for researchers and practitioners seeking to implement responsible and effective ML-driven workforce analytics.

Offloading footwear is a critical intervention for preventing and treating diabetic foot ulcers (DFUs), as it reduces plantar pressure (PP) and promotes healing. However, decision-making around its prescription remains fragmented, with variability in feature selection, limited personalization, and diverse evaluation approaches. While guidelines, knowledge-based systems, and machine learning (ML) applications have explored elements of decision-making, no clinical decision support system (CDSS) currently exists to provide feature-level prescriptions. We conducted a narrative review of 45 studies, including 12 guidelines/protocols, 25 knowledge-based systems, and eight ML applications. Studies published from inception to August 2025 were included. Studies were thematically analyzed by type of knowledge, decision logic, evaluation methods, and associated technologies. Guidelines emphasize PP thresholds (≤200 kPa or ≥25–30% reduction) but lack detailed outputs. Knowledge-based systems apply rule-based and sensor-driven logic, integrating PP monitoring, adherence tracking, and usability testing. ML applications introduce predictive classification, optimization, and generative models, achieving high computational accuracy but with limited explainability and clinical validation. Evaluation practices remain fragmented: biomechanical testing dominates protocols, usability and adherence assessments are common in knowledge-based systems, while ML studies emphasize technical accuracy without linking to long-term clinical outcomes. From this synthesis, we propose a five-component framework for CDSS development that discusses: (1) minimum viable dataset, (2) hybrid decision architecture combining rules, optimization, and explainable ML, (3) structured feature-level outputs, (4) continuous validation and evaluation, and (5) integration into clinical and telehealth workflows. The proposed framework presents a pathway to transform fragmented approaches into scalable, patient-centered CDSSs. Prioritizing interoperable datasets, explainable models, and outcome-driven evaluation will be critical for clinical adoption and improved DFU care.

The use of machine learning (ML) applications in the intensive care units (ICUs) has surged over the last two decades. This is the result of the digital transformation that many health-care organizations have implemented. Data that are generated in the process of intensive care have more volume, velocity, and value than data generated in any other general hospital's department. This characteristic of ICUs makes them attractive environments for developing models that require rich dataset. ML has been used to develop clinical decision support system (CDSS) that could make informative decisions without requiring prior in-depth knowledge about the roots of the disease or common characteristics of the patients. The adoption of ML-based CDSS in ICUs is continuously increasing as ML algorithms achieve high levels of accuracy in descriptive, diagnostic, predictive, and prescriptive decisions. This article reviews some of the applications of ML in ICUs. This article will show examples of how ML was used for outcome predictions, such as predicting mortality and readmission. Examples in this article also include using ML for diagnostic and image recognition purposes. This review will discuss the use of ML for monitoring ICU patients, whether monitoring their physical safety with artificial intelligence vision detection algorithms, monitoring their continuous bedside measurements, or monitoring the administration and dosage of their medications. All these examples show that ML-based CDSS are on the path for a journey full of innovative and creative solutions that will increase the quality, efficiency, and effectiveness of critical care.

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.