Navigating Ethical Dilemmas: The Role of AI in Supply Chain Decision-Making
Bibliographic record
Abstract
Current times in which supply chains are increasingly viewed as being uncouth in the practice of their operations call for the integration of AI to thereby solve ethical dilemmas within such chains. This paper delves into the role played by AI to navigate ethical dilemmas in supply chains, thereby discussing its ability to resolve challenges such as labor rights, environmental sustainability, and responsible sourcing. Through this literature review, the current research is able to draw on existing work on AI applications within the supply chain and highlight gaps concerning ethical implications. The paper illustrates the real benefits and challenges surrounding the application of these technologies through case studies of those organizations which successfully implement AI-driven tools for ethical decision-making. The framework proposed should, therefore, bring about actionable recommendations to the business on attaining such a balance between operational efficiency and ethical responsibility. Lessons contained in the overall suggest the necessary use of AI to construct a more transparent and accountable supply chain landscape but lead to a more sustainable and ethically sound business landscape
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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 | 0.058 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".