The Ethical Dangers of Deliberative Decision Making
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
Research on ethical decision making has been heavily influenced by normative decision theories that view intelligent choices as involving conscious deliberation and analysis. Recent developments in moral psychology, however, suggest that moral functions involved in ethical decision making are metaphorical and embodied. The research presented here suggests that deliberative decision making may actually increase unethical behaviors and reduce altruistic motives when it overshadows implicit, intuitive influences on moral judgments and decisions. Three lab experiments explored the potential ethical dangers of deliberative decision making. Experiments 1 and 2 showed that deliberative decision making, activated by a math problem-solving task or by simply framing the choice as a decision rather than an intuitive reaction, increased deception in a one-shot deception game. Experiment 3—which activated systematic thinking or intuitive feeling about the choice to donate to a charity—found that deliberative decision making could also decrease altruism. These findings highlight the potential ethical downsides of a rationalistic approach toward ethical decision making and call for a better understanding of the intuitive nature of moral functioning.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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