Towards Ethics in the Age of Artificial Intelligence: A Study of STEM Students' Perceptions of AI Ethics in Nigerian Universities
Bibliographic record
Abstract
Abstract Artificial Intelligence (AI) is fast transforming societies across the world and poses serious ethical issues concerning biases, privacy, accountability and job losses in the future. Although international bodies like the UNESCO Ethics of Artificial Intelligence Recommendation, the European Union Trustworthy AI Guidelines, and the U.S. AI Bill of Rights have paid particular attention to the idea of fairness, transparency, and human control, little is understood about how students in developing countries view the topics. This paper examines the awareness and perceptions of AI ethics among STEM students in Nigerian higher education institutions, where there is an increasing use of AI, but little AI ethics education. The study was based on the use of a cross-sectional survey of 300 students in three universities with a combination of quantitative measures of awareness and attitudes and related to qualitative thematic insights. Results show that there is moderate knowledge of AI ethics concepts: data privacy (80%) and job displacement (70%) were well known, yet fewer students were familiar with algorithmic bias (60%), transparency (45%), or human oversight (50%). Seven common themes in the qualitative responses were identified: privacy and surveillance, bias, job loss, misinformation, accountability, autonomy, and academic integrity. The cautious optimism of students was, however, accompanied by a significant lack of confidence in the reliability of AI in the future (77%), as well as the paramount support of regulation (88%) and ethics-by-design approaches (85%). Interestingly, 80% of the respondents complained that their curricula had failed to equip them with knowledge about the ethical issues surrounding AI, with informal information sources coming to the rescue. The study recommends that the implementation of AI ethics in STEM education, the alignment of curricula to international standards, and the contextualization of AI ethics with African philosophies are the three necessary steps to ensure that Nigeria produces not only consumers of AI, but ethically-enlightened innovators.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".