Further Thoughts on Defining f(x) for Ethical Machines: Ethics, Rational Choice, and Risk Analysis
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
There is a tendency to anthropomorphize artificial intelligence (AI) and reify it as a person. From the perspective of machine ethics and ethical AI, this has resulted in the belief that truly autonomous ethical agents (i.e., machines and algorithms) can be defined, and that machines could, by themselves, behave ethically and perform actions that are justified from a normative standpoint. Under this assumption, and given that utilities and risks are generally seen as quantifiable, many scholars have seen consequentialism (utilitarianism) and rational choice theory as likely candidates to be implemented in automated ethical decision procedures, for instance to assess and manage risks as well as maximize expected utility. Building on a recent example from the machine ethics literature, we use computer simulations to argue that technical issues with ethical ramifications leave room for reasonable disagreement even when algorithms are based on ethical and rational foundations such as consequentialism and rational choice theory. By doing so, our aim is to illustrate the limitations of automated behavior and ethical AI and, incidentally, to raise awareness on the limits of so-called ethical agents.
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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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