The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Diagnosing and Addressing Emergent Harms in the Design Process of Public AI and Algorithmic Systems
Open accessAbstract
Algorithmic and data-driven systems are increasingly used in the public sector to improve the efficiency of existing services or to provide new services through the newfound capacity to process vast volumes of data. Unfortunately, certain instances also have negative consequences for citizens, in the form of discriminatory outcomes, arbitrary decisions, lack of recourse, and more. These have serious impacts on citizens ranging from material to psychological harms. These harms partly emerge from choices and interactions in the design process. Existing critical and reflective frameworks for technology design do not address several aspects that are important to the design of systems in the public sector, namely protection of citizens in the face of potential algorithmic harms, the design of institutions to ensure system safety, and an understanding of how power relations affect the design, development, and deployment of these systems. The goal of this workshop is to develop these three perspectives and take the next step towards reflective design processes within public organisations. The workshop will be divided into two parts. In the first half we will elaborate the conceptual foundations of these perspectives in a series of short talks. Workshop participants will learn new ways of protecting against algorithmic harms in sociotechnical systems through understanding what institutions can support system safety, and how power relations influence the design process. In the second half, participants will get a chance to apply these lenses by analysing a real world case, and reflect on the challenges in applying conceptual frameworks to practice.
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A sociotechnical systems view of LLMs suggests that safety criteria and ethical assessments need to be situated, deliberated, and negotiated in the context of use, and span all layers of the sociotechnical system, including through organisational and institutional interventions.
“A sociotechnical systems view of LLMs suggests that safety criteria and ethical assessments need to be situated, deliberated, and negotiated in the context of use, and span all layers of the sociotechnical system, including through organisational and institutional interventions (Nouws et al., 2023; Aler Tubella et al., 2023; Dobbe & Wolters, 2024).”
From Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback (2025) · cites this paper as Nouws et al., 2023
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Self-citationDismantling Digital Cages: Examining Design Practices for Public Algorithmic Systems
Nouws, S., Janssen, M., & Dobbe, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15086-9_20 · Cited by 11
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Dahlgren Lindström, A., Methnani, L., Krause, L., Ericson, P., de Rituerto de Troya, Í. M., Coelho Mollo, D., & Dobbe, R. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09837-2 · Cited by 54
“…safety criteria and ethical assessments need to be situated, deliberated, and negotiated in the context of use, and span all layers of the sociotechnical system, including through organisational and institutional interventions (Nouws et al., 2023; Aler Tubella et al., 2023; Dobbe & Wolters, 2024).”
Self-citationOpen-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models
Sicari, S., Moreno, J. F. C., Rizzardi, A., & Coen‐Porisini, A. (2024) · ACM Computing Surveys · doi:10.1145/3703454 · Cited by 13
Misabstraction in Sociotechnical Systems
de Troya, Í. M. D. R., Kernahan, J., Doorn, N., Dignum, V., & Dobbe, R. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732122 · Cited by 6
Self-citationKleiman, F., & Barbosa, M. M. (2024) · Digital Government Research and Practice · doi:10.1145/3700141 · Cited by 4
Metadata
- Authors
- Nouws, S., Martinez De Rituerto De Troya, Í., Dobbe, R., & Janssen, M.
- Affiliations
- Delft University of Technology (NL)
- Delft University of Technology, Netherlands
- Venue
- Proceedings of the 24th Annual International Conference on Digital Government Research
- Publisher
- ACM
- Published
- 2023-07-11
- Pages
- 679-681
- ISSN
- 3050-8681 (ISSN-L)
- OpenAlex
- W4383752090
- Cited by
- 4
- References
- 8
- Citations by year
- 2025: 2 · 2024: 2
- FWCI
- 1.00
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Annual International Conference on Digital Government Research (publishedVersion, cc-by-nc-sa)
- Lirias (KU Leuven) (submittedVersion, cc-by-nc-nd)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- algorithmic harms, public sector AI, algorithmic systems, reflective design, system safety, institutions, power relations, socio-technical systems, discrimination, civilian protection, public sector organizations, design process
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