Bibliographic record
Abstract
Over the last few years, various public, private, and NGO entities have adopted a staggering number of non-binding ethical codes to guide the development of artificial intelligence. However, this seemingly failed to drive better ethical practices within AI organizations. In light of this observation, this paper aims to reevaluate the roles the ethics of AI can play to have a meaningful impact on the development and implementation of AI systems. In doing so, we challenge the notion that AI ethics should focus primarily on instilling ethical principles in practitioners within AI organizations, as well as the claim that AI ethics can only lead to ethics washing. We propose a two-pronged institutionalist approach to AI ethics, focusing on shaping organizational decision-making processes and emphasizing the necessity of binding legal regulations. First, we argue that AI ethics should give priority to institutional design over the internalization of ethical principles by individual practitioners. We then contend that legally binding rules are needed to this end, both as a motivation for organizations and to contribute to the semantic determination of high-level ethical principles. We then show that promising proposals to operationalize ethical principles require the backing of binding legal norms to be effective. We conclude by highlighting the potential of AI ethics to contribute meaningfully to legislative innovation in AI governance.
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.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".