Do Less Teaching, Do More Coaching: Toward Critical Thinking for Ethical Applications of Artificial Intelligence
Bibliographic record
Abstract
There have been discussions suggesting an ethics committee be established which would oversee humanity’s efforts in Artificial Intelligence (AI) and its applications to our society. This concern arises mostly because of the limitations of existing data used for the development of AI algorithms that intrinsically reflect unfair and discriminatory factors of the real world in which we live. However, it is hard to find a paper that philosophically addresses a pedagogical issue about the necessary shift from strict teaching to informative guidance: i.e., a conversation about developing the discernment and critical thinking skills which would allow people who use AI-integrated services to themselves monitor the AI’s ethical applications and thus secure the well-being of our society. This paper is differentiated from other papers in that it sheds light on the social problems that can arise if people become uncritically compliant with unethical and indiscriminate applications of AI, and it conveys the lesson that contemporary ordinary citizens should be alert to these pitfalls as well.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".