A Rossian Method for Applying Principles in AI
Bibliographic record
Abstract
In the past several years there has been a rapid development of new technologies, applications, organizations, and institutions in the area of artificial intelligence (AI). At the same time, ethical reasoning about AI has not been able to keep up with the speed of these advances. As a result, developers are left to rely on existing rules, professional codes, policies and personal ethics which may not provide the appropriate guidance about ethical conduct and may require greater specificity (O'Leary). Many commentators have acknowledged the need for a clearer understanding of ethical values and principles to guide AI research. Drawing on insights from the formation of the field of biomedical ethics, we argue that AI ethics should make use of the method based on prima facie duties derived from W.D. Ross’ approach to ethics. A Rossian approach has proved influential in biomedical ethics. We further propose a modification to the list of principles proposed by Floridi and Cowls, arguing that the principles of explicability and accountability should be separated for ease of application. We argue that this method of applying principles is just what has been missing in AI ethics and is the crucial link between the now common lists of principles and putting them into practice in a way that can inform actual developments on the ground.
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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 | 0.070 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".