Ethics-by-Design Models for Socio-Technical Systems

Authors

  • Lea Bianchi Senior Lecturer, Department of Machine Learning, Advanced Computing University, Paris, France Author https://orcid.org/7443-3843-3140-2404
  • Elena Garcia Professor, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia Author

Keywords:

ethics-by-design, value-sensitive design, responsible innovation, socio-technical systems, participatory design, trustworthy AI, privacy engineering, algorithmic governance

Abstract

Ethics-by-design (EbD) approaches to socio-technical system development integrate ethical considerations into the engineering process from the earliest design stages, rather than applying ethics as a retrospective audit or regulatory compliance exercise after systems are built and deployed. Socio-technical systems -- in which technical components (algorithms, sensors, actuators, databases) and social components (human roles, organisational processes, institutional structures, cultural norms) are mutually constitutive -- require ethics-by-design approaches that simultaneously address technical specification and social embeddedness, rather than treating ethics as a technical constraint applied to an otherwise value-neutral system. This paper proposes the Ethics-by-Design for Socio-Technical Systems Framework (EbDST), a systematic comparison of five EbD models -- value-sensitive design (VSD), responsible research and innovation (RRI), trustworthy AI by design (TAID), privacy engineering, and participatory ethics design (PED) -- across four socio-technical system domains: AI-mediated healthcare, algorithmic governance, smart city infrastructure, and digital labour platforms. EbDST evaluates each model across five process dimensions: stakeholder inclusion, technical integration depth, iterative refinement, power asymmetry awareness, and real-world implementation evidence. Key results: participatory ethics design achieves the highest overall EbD quality score (0.892) due to deepest stakeholder inclusion and power asymmetry awareness; trustworthy AI by design achieves the highest technical integration depth (0.924); privacy engineering achieves the most mature implementation evidence base. The framework provides an EbD model selection guide and a combined EbDST process for practical deployment.

Author Biographies

  • Lea Bianchi, Senior Lecturer, Department of Machine Learning, Advanced Computing University, Paris, France

    Senior Lecturer, Department of Machine Learning, Advanced Computing University, Paris, France

  • Elena Garcia, Professor, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia

    Professor, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia

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Published

2024-03-24

How to Cite

Ethics-by-Design Models for Socio-Technical Systems. (2024). Journal of Ethics in Emerging Technologies & Society P-ISSN 3117-5996 and E-ISSN 3117-6003, 1(1), 9-16. https://galaxiauniverse.com/index.php/JEETS/article/view/408