Abstract
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government's and its citizens' relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp-mi, smc-chi(2) and mc-chi(2) conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.

Abstract
Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time-series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one-class and binary classification algorithms. This study aims to classify time-series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time-series data to train both one-class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade-off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one-class and binary classification ultimately relies on dataset's features and task objectives.

Abstract

Abstract

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.