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dc.titleAI and Judicial Productivity: The Impact of MIDAS on the Courts of Fortaleza, Brazil
dc.contributor.authorPierri, Gastón
dc.contributor.authorFontenele, Marcelo
dc.contributor.authorNunes, Jose Luiz
dc.contributor.orgunitInstitutional Capacity of the State Division
dc.contributor.orgunitInstitutions for Development Sector
dc.coverageBrazil
dc.date.available2026-07-23T00:07:00
dc.date.issue2026-07-23T00:07:00
dc.description.abstractThis paper presents preliminary results from a pilot study conducted in the courts of Ceará, Brazil. The study evaluates the impact of introducing a tool that uses natural language processing and machine learning techniques to cluster judicial acts by textual similarity on clerk productivity, measured as the number of case files a clerk can produce in a day. Estimates indicate that treatment-group clerks produced approximately 10 more case files per day than control-group clerks, a statistically significant difference equivalent to a 37% increase relative to the control group mean. The results are robust to the exclusion of outlier observations and exceptionally productive clerks.
dc.format.extent23
dc.identifier.doihttp://dx.doi.org/10.18235/0014427
dc.identifier.urlhttps://publications.iadb.org/publications/english/document/AI-and-Judicial-Productivity-The-Impact-of-MIDAS-on-the-Courts-of-Fortaleza-Brazil.pdf
dc.language.isoen
dc.publisherInter-American Development Bank
dc.subjectProductivity
dc.subjectLabor Force
dc.subjectArtificial Intelligence
dc.subjectJustice Administration
dc.subjectNatural Language Processing
dc.subjectCriminal Justice
dc.subjectClosing
dc.subjectMachine Learning
dc.subject.jelcodeO33 - Technological Change: Choices and Consequences • Diffusion Processes
dc.subject.jelcodeH83 - Public Administration • Public Sector Accounting and Audits
dc.subject.jelcodeK40 - Legal Procedure, the Legal System, and Illegal Behavior: General
dc.subject.jelcodeC93 - Field Experiments
dc.subject.jelcodeJ24 - Human Capital • Skills • Occupational Choice • Labor Productivity
dc.subject.keywordsartificial intelligence;Judicial Productivity;Natural Language Processing;Court Administration;Public Sector Automation;machine learning;Field experiment;access to justice
dc.typeWorking Papers
idb.identifier.pubnumberIDB-WP-01856
idb.operationRG-T4629
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