Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/118516
Title: SynthEthics: Ensuring Digital Ethics and Performance with a Design Theory for Using Synthetic Image Data in Digital Health Deep Learning
Author(s): Böhmer, Martin
Kuehnel, Stephan
Damarowsky, Johannes
Brendel, Alfred Benedikt
Issue Date: 2025-08
Type: Preprint
Language: English
Publisher: Universitäts- und Landesbibliothek Sachsen-Anhalt
Subjects: Artificial Intelligence
Digital Ethics
Synthetic Data
Deep Learning
Design Theory
Abstract: This paper addresses the need for ethical and effective use of synthetic image data in digital health computer vision. It explores the design requirements and design principles for both responsible use of artificial intelligence in digital health and model robustness, focusing on privacy, ethical compliance, and domain adaptation. Using the design science research paradigm along with value-sensitive design and sociotechnical systems theory, this study presents a design theory that provides actionable guidance for the generation, selection, and integration of synthetic data in digital health. Through heuristic theorizing over two design cycles, the work provides a robust theory artifact and conceptual model to ensure ethical use and improve model performance in digital health through appropriate domain adaptation, generalization, and accuracy. In addition to contributing to theoretical knowledge, this research offers practical implications for health authorities to promote ethical standards and performance in synthetically trained AI applications.
URI: https://opendata.uni-halle.de//handle/1981185920/120474
http://dx.doi.org/10.25673/118516
DOI: Assigned in the course of publication by the ACM Digital Library.
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Journal Title: The DATA BASE for Advances in Information Systems
Appears in Collections:Lehrstuhl für Betriebliches Informationsmanagement

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