Balancing Bytes and Ethics: Stakeholder Implications of Private LLMs
Bibliographic record
Abstract
This research explores the ethical implications of private large language models (PLLMs) through the lens of stakeholder theory. Private LLMs, tailored for specific organizational needs, present unique privacy and data protection challenges. We examine the historical development of LLMs and their impact on stakeholders, including shareholders, employees, customers, and society. Our proposed framework balances stakeholder interests with ethical considerations, offering a comprehensive approach to the ethical development and deployment of PLLMs. This framework emphasizes transparency, accountability, and sustainable practices to ensure long-term value creation. Future research directions include developing regulatory frameworks, conducting detailed social impact assessments, and exploring strategies for effective human-AI collaboration. This study contributes to academic discourse by providing a multi-faceted approach to managing the ethical challenges posed by PLLMs, fostering best practices, and mitigating potential conflicts among stakeholders.
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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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".