Abstract
The increasing use of Automated Machine Learning (AutoML) in information systems development shortens development cycles and improves the short-term efficiency of analytical components, while delegating key design decisions to automated mechanisms. This paper argues that this practice may create a distinct form of technical debt: automation-induced technical debt. A lifecycle-oriented conceptual framework is proposed, identifying four dimensions of this debt: data, model opacity, governance, and integration, and its accumulation across successive phases of the system life cycle. The framework supports systematic debt management in AutoML projects and sustainable AI-driven systems.
Paper Type
Poster
DOI
10.62036/ISD.2026.2
Automation-Induced Technical Debt in Sustainable IS Development: Implications of AutoML Adoption
The increasing use of Automated Machine Learning (AutoML) in information systems development shortens development cycles and improves the short-term efficiency of analytical components, while delegating key design decisions to automated mechanisms. This paper argues that this practice may create a distinct form of technical debt: automation-induced technical debt. A lifecycle-oriented conceptual framework is proposed, identifying four dimensions of this debt: data, model opacity, governance, and integration, and its accumulation across successive phases of the system life cycle. The framework supports systematic debt management in AutoML projects and sustainable AI-driven systems.
Recommended Citation
Niedbał, R.(2026). Automation-Induced Technical Debt in Sustainable IS Development: Implications of AutoML Adoption. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.2