Leveraging AI in the Kenyan Judiciary: A Case for Utilizing Text Classification Models for Data Completeness in Case Law Meta Data in Kenya’s Employment and Labor Relations Court
Bibliographic record
Abstract
Abstract AI has been revolutionary in improving different professional fields. In the legal sector, AI is utilized, in a number of jurisdictions, for different purposes both at the bar and bench level. The study investigates the efficacy of an AI algorithm in completing missing data in digitized documents, i.e., how AI can be utilized to achieve data completeness of precedents in the judiciary through text classification in order to achieve an optimal foundational basis for the creation of data sets that will facilitate the utilization of AI for different purposes. The Employment and Labor Relations court is used as a case study. The study analyzed the efficacy of 5 text classifier models: passive aggressive, linear regression, decision tree, random forest, and support vector machine (SVM) model. The results obtained from the study show that text classification can be automated successfully using machine learning techniques to generate case metadata. The accuracy of the text classifier methods utilized in the study range between 82% and 98%. Despite the data limitations faced in this study, the results obtained help increase confidence that advanced NLP techniques have matured enough to be applicable to legal text in the Kenyan Judiciary. Findings from the study suggest that the success rates of the text classifier techniques are not merely dependent on text content, but the context of this content is also a determining factor - the nature of the cases and the structure of the legal system play an important role in the performance of text classifier models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".