Abstract

Abstract
In recent years, the EU has been pushing forward ground-breaking legislation that covers new digital environments and services, with a strong focus on Ethics and AI. This includes legislation such as the Artificial Intelligence Act, the Digital Services Act or the General Data Protection Regulation. This legislation is, however, often written in very general and high-level terms, leaving a lot of space for interpretation, and a gap concerning how it could or should be implemented, realistically. In this paper we look specifically at the principle of Transparency in the Digital Services Act. Specifically, we discuss the requirements concerning Transparency in the regulation, we identify the gaps, and propose concrete measures that can be considered to facilitate and guide its implementation.

Abstract
With the proliferation of ODR service providers, there is a critical necessity to establish mechanisms supporting their functioning, particularly while designing ODR processes. This article aims to examine the impact of process modelling using BPMN, and of its relevance in the integration of AI into ODR processes within the EU. BPMN allows a meticulous depiction of all the ODR process steps, stakeholders, and underlying data in structured formats that are readable and interpretable by both humans and AI, which enables its integration. The advantages include predictive analysis, identification of opportunities for continuous improvement, operational efficiency, cost and time reduction, and enhanced accessibility for self-represented litigants. Additionally, the transparency afforded by explicitly incorporating AI in BPMN notation fosters a clearer comprehension of processes, facilitating management and informed decision-making. Nevertheless, it remains imperative to address ethical concerns such as algorithmic bias, fairness, and privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
As LLMs gain an increasingly relevant role and agency, their alignment with human values, principles and goals is crucial for their responsible deployment and acceptance. The main goal of this study is to assess the alignment of different LLMs regarding the relative importance of AI Ethics principles across different domains. To this end, human experts in different domains were asked, through a questionnaire, to rate the relative importance of six AI Ethics principles in their respective domains, totaling 6 domains. Then, five publicly available LLMs were asked to rate the same Ethics principles in different domains. Multiple prompts were used multiple times, to also evaluate consistency, totaling 90 runs per LLM. Model alignment was measured through the correlation with human experts, and consistency was evaluated through the standard deviation. Results show varying degrees of alignment and consistency, with a couple of models showing satisfactory results. This makes it possible to envisage the use of such models to automatically configure and adapt data pipeline ecosystems and architectures across different domains, selecting processes, dashboard elements or monitored KPIs according to the target domain or the goals of the system.

Abstract
Explainable AI (xAI) emerged as one of the ways of addressing the interpretability issues of the so-called black-box models. Most of the xAI artifacts proposed so far were designed, as expected, for human users. In this work, we posit that such artifacts can also be used by computer systems. Specifically, we propose a set of metrics derived from LIME explanations, that can eventually be used to ascertain the quality of each output of an underlying image classification model. We validate these metrics against quantitative human feedback, and identify 4 potentially interesting metrics for this purpose. This research is particularly useful in concept drift scenarios, in which models are deployed into production and there is no new labelled data to continuously evaluate them, becoming impossible to know the current performance of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Many of today’s domains of application of Machine Learning (ML) are dynamic in the sense that data and their patterns change over time. This has a significant impact in the ML lifecycle and operations, requiring frequent model (re-)training, or other strategies to deal with outdated models and data. This need for dynamic and responsive solutions also has an impact on the use of computational resources and, consequently, on sustainability indicators. This paper proposes an approach in line with the concept of Frugal AI, whose main aim is to minimize the resources and time spent on training models by re-using models from a pool of past models, when appropriate. Specifically, we present and validate a methodology for similarity-based model selection in data streaming environments with concept drift. Rather than training a new model for each new block of data, this methodology considers a pool with only a subset of the models and, for each new block of data, will select the best model from the pool. The best model is determined based on the distance between its training data and the current block of data. Distance is calculated based on a set of meta-features that characterizes the data, and on the Bray-Curtis distance. We show that it is possible to reuse previous models using this methodology, leading to potentially significant saving of resources and time, while maintaining predictive quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.