Automated ethical decision, value-ladenness, and the moral prior problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Part of the literature on machine ethics and ethical artificial intelligence focuses on the idea of defining autonomous ethical agents able to make ethical choices and solve dilemmas. While ethical dilemmas often arise in situations characterized by uncertainty, the standard approach in artificial intelligence is to use rational choice theory and maximization of expected utility to model how algorithm should choose given uncertain outcomes. Motivated by the moral proxy problem , which proposes that the appraisal of ethical decisions varies depending on whether algorithms are considered to act as proxies for higher- or for lower-level agents, this paper introduces the moral prior problem , a limitation that, we believe, has been genuinely overlooked in the literature. In a nutshell, the moral prior problem amounts to the idea that, beyond the thesis of the value-ladenness of technologies and algorithms, automated ethical decisions are predetermined by moral priors during both conception and usage. As a result, automated decision procedures are insufficient to produce ethical choices or solve dilemmas, implying that we need to carefully evaluate what autonomous ethical agents are and can do, and what they aren’t and can’t.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it