Designing Accountable Systems
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Abstract (arXiv)
Accountability is an often called for property of technical systems. It is a requirement for algorithmic decision systems, autonomous cyber-physical systems, and for software systems in general. As a concept, accountability goes back to the early history of Liberalism and is suggested as a tool to limit the use of power. This long history has also given us many, often slightly differing, definitions of accountability. The problem that software developers now face is to understand what accountability means for their systems and how to reflect it in a system's design. To enable the rigorous study of accountability in a system, we need models that are suitable for capturing such a varied concept. In this paper, we present a method to express and compare different definitions of accountability using Structural Causal Models. We show how these models can be used to evaluate a system's design and present a small use case based on an autonomous car.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 3; 2025: 1; 2026: 1.
7 papers.
- 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learningcited by 47
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
- 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
- 2025Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challengescited by 3
- 2024Towards Equitable Agile Research and Development of AI and Roboticscited by 1
- 2024Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Gridcited by 0
- 2026ProToken: Token-Level Attribution for Federated Large Language Modelscited by 0
- 2026ProToken: Token-Level Attribution for Federated Large Language Models27 Jan 2026
- 2025Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges18 Nov 2025
- 2024Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Grid2 Aug 2024
- 2024Towards Equitable Agile Research and Development of AI and Robotics13 Feb 2024
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective9 Jan 2024
- 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach25 Feb 2022
- 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning10 Feb 2022