Abstract
This workshop proposal is the third edition of a workshop which has been organised at EICS 2023 and EICS 2024. This edition aims to bring together researchers and practitioners interested in the engineering of interactive systems that embed AI technologies (as for instance, AI-based recommender systems) or that use AI during the engineering lifecycle. The overall objective is to identify (from experience reported by participants) methods, techniques, and tools to support the use and inclusion of AI technologies throughout the engineering lifecycle for interactive systems. A specific focus is on guaranteeing that user-relevant properties such as usability and user experience are accounted for. Contributions are also expected to address system-related properties, including resilience, dependability, reliability, or performance. Another focus is on the identification and definition of software architectures supporting this integration.

Abstract
The development of artificial intelligence (AI) in the last decade has reshaped government operations and raised privacy concerns as automated processes become commonplace. This study aims to identify the main privacy issues associated with government use of AI in public services. Using a bibliometric analysis that includes co-citation of references and authors, bibliographic coupling, and keyword co-occurrence approaches, the study analyzed the literature on this topic through VOSViewer and the Web of Science database. Findings highlight significant privacy concerns: (i) opaque data-driven decisions, (ii) bias in predictive algorithms, (iii) difficulty obtaining explanations for decisions, (iv) mistrust in AI systems, (v) ethical lapses in AI execution, and (vi) trust deficit in government AI use. Additionally, 18 research questions are defined, addressing ethical limits of privacy in AI government use. A consensus in the literature urges governments to enact laws ensuring data privacy "by default" in AI decision-making and data management/transfer to third parties. © The Editor and Contributing Authors Severally 2025. All rights reserved.

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This paper introduces a passive acoustic method for detecting and tracking marine vessels via Time Difference of Arrival (TDoA) estimates collected by an array of synchronized Intelligent Buoys (SIBs). A 24-hour deployment recorded a passing tanker's acoustic signature, which we processed with band-pass filtering and sliding-window cross-correlation, to extract robust TDoA time series. We implemented a nonlinear Gauss-Newton estimator to reconstruct the vessel's trajectory. Position tracking fails, given the geometric configuration of the SIBs with regard to the vessel's trajectory but we suggest a possible solution to overcome this problem using synthesized data inspired on the experiment.

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