Initiative file / Judicial
ML model for budget execution forecasting
Evidence record v2
02 / 31
- Institution
- Judicial Branch
- Editorial classification
- Verified adoption
- Initiative type
- AI system
- Documented phase
- Operational
- Relationship to AI in the source
- Explicit relationship to AI
- Execution evidence
- Confirmed
- First evidence
- Last verified
- Next review: Nov 19, 2026
Documented scope
What it is
Machine-learning model used by the Judicial Branch to predict budget execution from its historical behavior. The institution’s 2019 accountability report documents its use and associated savings.
Objective stated by the institution
Predict the percentage of budget execution from its historical behavior.
Known timeline
Only milestones with an explicit date in the sources or editorial review are shown.
- First evidence
- Operational start
- Latest editorial verification
- Next scheduled review
Evidence reading
Each dimension is assessed separately. Undetermined marks an evidence gap; not applicable means the dimension does not correspond to the documented object.
What is confirmed
Confirmed evidence is detailed in the matrix and documented results; there are no additional notes in this field.
What could not be determined
- No later public metrics updating cumulative savings were found.
Documented results
Results are published only when a source traceably attributes them to this initiative.
The institutional accountability report stated savings above USD 379,965 associated with the model.
Evidence matrix
Sources and traceability
Sources state which claims they support, their origin and the date on which they were consulted.
- [1]Judicial Branch 2019 accountability report
- Publisher
- Poder Judicial de Costa Rica
- Source type
- Official primary source
- Published
- Consulted
Supports: existence · execution · AI technique · operational use · reported result
Open questions
- What validation, human-review and error-monitoring controls does the budget model currently use?
Context
The primary source confirms the technique, use and a reported financial result. That amount is not extrapolated to later years.
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