The Essential Relationship between Information Ethics and Artificial Intelligence
Bibliographic record
Abstract
This article rethinks the position of Information Ethics (IE) vis-à-vis the growing discipline of the ethics of AI. While IE has a long and respected academic history, the discipline of the ethics of AI is much younger. The scope of the latter discipline has exploded in the last decade in sync with the explosion of data driven AI. Currently, the ethics of AI as a discipline can be said to have sub-divided at least into machine ethics, robot ethics, data ethics, and neuro ethics. The argument presented here is that ethics of AI can from one perspective be viewed as a sub-discipline of IE. IE is at the heart of ethical concerns about the potential de-humanising impact of AI technologies, as it addresses issues relating to communication, the status of knowledge claims, and the quality of media-generated information, among many others. Perhaps the single most concerning ethical concern in the context of data-driven AI technology is the rise of new social narratives that threaten humans’ special sense of agency and, and this is firstly an IE concern. The article thus argues for the independent position of IE as well as for its position as the core, over-arching discipline, of the ethics of AI.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".