There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees’ attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Naïve Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.

Wearable technology has advanced significantly, offering real-time monitoring of athletes’ physiological parameters and optimizing training and recovery strategies. Recent developments focus on biosensor devices capable of monitoring biochemical parameters in addition to physiological ones. These devices employ noninvasive methods such as sweat analysis, which reveals critical biomarkers like glucose, lactate, electrolytes, pH, and cortisol. These biomarkers provide valuable insights into an athlete’s energy use, hydration status, muscle function, and stress levels. Current technologies utilize both electrochemical and colorimetric methods for sweat analysis, with electrochemical methods providing higher precision despite potential signal interference. Wearable devices such as epidermal patches, temporary tattoos, and fabric-based sensors are preferred for their flexibility and unobtrusive nature compared to more rigid conventional wearables. Such devices leverage advanced materials and transmit real-time data to computers, tablets, or smartphones. These data would aid coaches and sports medical personnel in monitoring athletes’ health, optimizing diets, and developing training plans to enhance performance and reduce injuries.

Despite the existing skepticism about the use of automatic systems in contexts where human knowledge and experience are considered indispensable (e.g., the granting of a mortgage, the prediction of stock prices, or the detection of cancers), our work aims to show how the use of explainability and fairness techniques can lead to the growth of a domain expert’s trust and reliance on an artificial intelligence (AI) system. This article presents a system, applied to the context of loan approval processes, focusing on the two aforementioned ethical principles out of the four defined by the High-Level Expert Group on AI in the document “Ethics Guidelines for Trustworthy AI,” published in April 2019, in which the key requirements that AI systems should meet to be considered trustworthy are identified. The presented case study is realized within a proprietary framework composed of several components for supporting the user throughout the management of the whole life cycle of a machine learning model. The main approaches, consisting of providing an interpretation of the model’s outputs and monitoring the model’s decisions to detect and react to unfair behaviors, are described in more detail to compare our system within state-of-the-art related frameworks. Finally, a novel Trust & Reliance Scale is proposed for evaluating the system, and a usability test is performed to measure the user satisfaction with the effectiveness of the developed user interface; results are obtained, respectively, by the submission of the mentioned novel scale to bank domain experts and the usability questionnaire to a heterogeneous group composed of loan officers, data scientists, and researchers.