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TRUVACE RECORD VERSION
record: TRV-2026-0413
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T10:35:54.814880Z
status: published
lens: trace
sector: policy
headline: Justitia ex machina: The impact of an AI system on legal decision-making and discretionary authority
dek: Governments increasingly use algorithms to inform or supplant decision-making. Artificial Intelligence systems in particular are considered objective, consistent and efficient decision-makers, but have also been shown to be fallible. Furthermore, the adoption of artificial intelligence (AI) in government is fraught with challenges which are only partly understood and rarely studied in practice. In this paper, we draw on science and technology studies and human computer interaction and report on a critical case s…
gain_title: AI system for traffic violation appeals at a Dutch court can best be applied in support of legal decision-making and its process design may mitigate some risks of algorithmic decision-making.
problem_title: Use of the AI system for traffic violation appeals impacts decisions made by legal experts and creates tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms.
trace_subject: AI system for processing traffic violation appeals at a Dutch court and its effect on legal decision-making
gain_reading: AI system for traffic violation appeals at a Dutch court can best be applied in support of legal decision-making and its process design may mitigate some risks of algorithmic decision-making.
gain_evidence: AI systems can best be applied in support of legal-decision making | may mitigate some of the risks of algorithmic decision-making
problem_reading: Use of the AI system for traffic violation appeals impacts decisions made by legal experts and creates tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms.
problem_evidence: find that use of the AI systems impacts decisions made by legal experts | tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms
quick_read: Researchers conducted action research on the development and deployment of an AI system to process traffic violation appeals at a Dutch court, using interviews, observations, documents and a user-experiment to compare decisions made by, with and without the system.

The case matters because it shows government AI does not simply automate existing practice but reshapes expert discretion, raising questions about how to design support roles for AI in courts and whether lessons from traffic cases transfer to more complex legal domains.
limitation: Findings come from a single critical case study of traffic violation appeals at a Dutch court, limiting generalizability to other courts or case types.
tag: Automated dual reading
key_points: Study used canonical action research with interviews, observations, documents and a user-experiment during development of the AI system. | Analysis compared decisions made by, with and without the AI system for traffic violation appeals. | Authors frame findings as tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms.
rundown: The research team actively participated in building the system rather than only observing, collecting interviews, observations, documents and running a user-experiment to compare decision modes.

By June 2024 the authors reported that the traffic violation case workflow itself helped contain some algorithmic risks, while still showing measurable shifts in how legal experts decided when assisted by the system.
sources:
- peer_reviewed | Big Data & Society | https://doi.org/10.1177/20539517241255101 | 2024-06-01
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