The Utility of Artificial Intelligence in the Pursuit of Justice through Judicial Precedent in Nigeria
Bibliographic record
Abstract
Judicial Precedent is a cardinal aspect of administration of justice that ensures the certainty of case laws. Certainty of judicial positions removes vagaries and engenders specificity in the understanding and application of laws within the Court system. The emergence of Artificial Intelligence (AI) has been felt in almost, if not all aspects of life, including the judicial decision-making system. AI, which is a simulation of human intelligence processes by machines, especially computer systems, offers a paradigm shift in the administration of justice, from the traditional methods of solving legal tasks. Countries such as Germany, Estonia, USA, Canada and India have introduced different AI tools into their judicial system to drive seamless and predictable administration of justice. This study examined the role which AI could play in enhancing the efficiency of judicial precedent in the delivery of justice by Nigerian courts of record. With the deep and complex web of facts and laws that abound in cases submitted before judges for resolution, this paper advanced the prospect of integrating AI in the precedent induced judicial decision-making process, without undermining the autonomy which the Judges have in the exercise of their discretion in making reasoned decisions. This study employed the use of empirical, primary and secondary materials in concluding that judicial precedent as a concept will be more beneficial in the administration of justice, if AI is allowed to play a frontal role in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".