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Trust by design: An ethical framework for collaborative intelligence systems in industry 5.0

Merchán-Cruz, Emmanuel A.; Gabelaia, Ioseb; Savrasovs, Mihails; Hansen, Mark F.; Soe, Shwe; Rodriguez-Cañizo, Ricardo G.; Aragón-Camarasa, Gerardo

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Authors

Emmanuel A. Merchán-Cruz

Ioseb Gabelaia

Mihails Savrasovs

Mark Hansen Mark.Hansen@uwe.ac.uk
Professor in Machine Vision and Machine Learning

Profile image of Shwe Soe

Dr Shwe Soe Shwe.Soe@uwe.ac.uk
Associate Professor in Digital Manufacturing

Ricardo G. Rodriguez-Cañizo

Gerardo Aragón-Camarasa



Abstract

Industry 5.0 highlights human-centricity, sustainability, and resilience. This article presents a novel Trust by Design framework applicable to collaborative intelligence systems within Industry 5.0, addressing the need for collaborative systems to be reliable by design, incorporating ethical principles such as transparency, accountability, fairness, and privacy throughout the entire system lifecycle. The framework is grounded in select ethical philosophies applied to practical design requirements for human-AI collaboration, identifying key ethical challenges that threaten to damage trust and restrict the adoption of collaborative systems. The authors employ a qualitative, literature-driven method, conceptual modeling, and scenario-based case study analysis, synthesizing best practices and ethical policies from the EU AI Act, GDPR, and more. Trust by Design suggests a structured set of principles and implementation measures to embed ethics into every phase of the system’s lifecycle. The applicability and suitability of the framework are demonstrated through representative real-world application scenarios across industries. The results indicate that trust in collaborative intelligence systems is not static but dynamic, context-dependent, and controlled by transparency, fairness, and user experience. The framework includes instruments and methods to measure ethical performance, including trust metrics, override rates, fairness indicators, and incident tracking.

Journal Article Type Article
Acceptance Date May 5, 2025
Online Publication Date May 11, 2025
Publication Date May 11, 2025
Deposit Date May 13, 2025
Publicly Available Date May 13, 2025
Journal Electronics
Electronic ISSN 2079-9292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 10
Article Number 1952
DOI https://doi.org/10.3390/electronics14101952
Public URL https://uwe-repository.worktribe.com/output/14417152

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