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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".