Copyright in the age of artificial intelligence: Navigating access to algorithmic training materials and the three‐step test for text and data mining in Nigeria
Bibliographic record
Abstract
Abstract Over the past decade, the Nigerian government has sought to leverage Artificial Intelligence (AI) to drive socio‐economic transformation and improve the welfare of its citizenry. Recent initiatives, such as the establishment of the National Centre for AI and Robotics (NCAIR) and the development of several strategic AI policies, highlight the country's commitment to this objective. This article explores the often‐overlooked issue of how the Nigeria's copyright regime hinders these initiatives, revealing that the regime permits only fair dealing and the transient or incidental reproductions of copyrighted materials for limited technological purposes. This study argues that this regime is unduly restrictive for algorithmic training and risks stifling AI innovation and the development of machine‐learning models in Nigeria. It recommends adopting a bespoke text and data mining (TDM) exception tailored to Nigeria's needs, allowing the use of copyrighted works for training AI models and machine learning activities within defined limits. Drawing on comparative analyses of copyright frameworks in jurisdictions such as Singapore, Japan, the United Kingdom, and the European Union, this study demonstrates that the proposed TDM exception aligns with the three‐step test under international copyright conventions. For instance, the exception is limited to specific users and types of reproductions, applies only to internalized and transformative reproductions, and avoids traditional methods of exploiting copyrighted works that prejudice the legitimate interests of rightsholders. The ultimate goal of this exception is to recalibrate Nigeria's copyright system to justly balance AI innovation with authors' rights, aligning it with foundational principles of the international copyright system in an era of rapid technological advancements.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".