The rule of law or the rule of robots? Nationally representative survey evidence from Kenya
Bibliographic record
Abstract
With AI now passing the bar, and with increasing court caseloads worldwide hampering access to justice, there are calls for judges to make use of chatbots to help expedite their work. Such calls pose a normative question: whether our ideal of the rule of law is consistent with judicial reliance on computer generated legal research. In deciding whether artificial intelligence could support the administration of justice in this way, the views of those who stand to gain the most through more readily available dispute resolution will be critical. Collecting nationally representative survey data from Kenya, we report a vignette-based experiment on the acceptability of AI law clerks – assistants whose legal analysis does not decide what the law says but which informs the ultimate decision. We find that an AI’s influence on the law’s application is seen as no less legitimate than that of a human assistant. This result spurs efforts to systematically investigate whether the integration of AI might make justice systems more efficient, accessible, and trustworthy in practice.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".