The challenges of being an in-house AI ethicist and how to overcome them
Bibliographic record
Abstract
The ‘institutional turn’ to AI ethics signifies the establishment of the profession of an in-house AI ethicist. Reflecting on my own experience working as an in-house AI ethicist at an academic institution, this essay discusses three challenges of the profession: ambiguous objectives, conflict of interest, and epistemological differences. With these three challenges, the job requires performing several roles in parallel (i.e. auditor, educator, researcher, collaborator), coping with different stakeholders, and overcoming disciplinary approaches to AI ethics. Ultimately, the in-house AI ethicist participates in a balancing act, in which they have to constantly question their own positionality in relation to the institutional context. To overcome these challenges, practitioners could approach the job as ethnographic fieldwork.
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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 | 0.075 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.075 |
| Scholarly communication | 0.035 | 0.041 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".