Bibliographic record
Abstract
Abstract Personal and professional relationships between people take a wide variety of forms, with many including both socially and legally enforced powers, responsibilities, and protections. Artificial intelligence (AI) systems are increasingly supplementing, or even replacing, people in such roles including as advisors, assistants, and (soon) doctors, lawyers, and therapists. Yet it can be quite unclear to what degree they are bound by the same sorts of responsibilities. Much has been written about fairness, accountability, and transparency in the context of AI use and trust. But largely missing from this conversation is the concept of “AI loyalty”: for whom does an AI system work? AI systems are often created by corporations or other organizations, and may be operated by an intermediary party, such as a government agency or business, but the end-users are often distinct individuals. This leads to potential conflict between the interests of the users and those of the creators or intermediaries, and, problematically, to AI systems that appear to act purely in users’ interest even when they are not. Here, we investigate the concept of “loyalty” both in human and AI systems, and advocate its central consideration in AI design. Systems for which high loyalty is appropriate should be designed, from the outset, to primarily and transparently benefit their end users, or at minimum transparently communicate unavoidable conflict-of-interest tradeoffs. This chapter discusses both market and social advantages of high-loyalty AI systems, and potential governance frameworks in which AI loyalty can be encouraged and—in appropriate contexts—required.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".