Ethical Challenges in AI-Powered Supply Chains: A U.S.-Nigeria Policy Perspective
Bibliographic record
Abstract
The incorporation of artificial intelligence (AI) into global supply chains is revolutionizing industries by increasing productivity, cutting costs, and improving decision-making. However, the adoption of AI in supply chains also presents significant ethical challenges, especially when comparing advanced economies like the United States and developing economies like Nigeria. This study examines the ethical issues that arise from AI-powered supply chains through a comparative policy lens, focusing on the U.S. and Nigeria. In the U.S., ethical concerns center on privacy, data security, algorithmic transparency, and the possibility of job displacement, while in Nigeria, additional challenges include infrastructure constraints, a lack of regulatory frameworks, and a digital divide that exacerbates the ethical implications of AI adoption in supply chains. This looks at how the policies of the two nations handle these problems, with the US highlighting the necessity of precise laws, moral standards, and corporate accountability in the application of AI. Nigeria's new AI laws, on the other hand, emphasize data governance, capacity building, and the development of an inclusive digital environment. The significance of customized policy solutions that take into account the distinct economic, social, and technical circumstances of each country is highlighted by examining these divergent approaches. The report also emphasizes the necessity of international collaboration in creating uniform ethical guidelines for artificial intelligence in global supply chains. The findings suggest that while AI holds the potential to revolutionize supply chains, it also necessitates careful policy planning and ethical oversight to ensure that its benefits are realized equitably and sustainably across different regions. Keywords: Ethical, Challenges, AI-powered, Supply chains, U.S, Nigeria, Policy perspective.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".