Lying is sometimes ethical, but honesty is the best policy: The desire to avoid harmful lies leads to moral preferences for unconditional honesty.
Bibliographic record
Abstract
People believe that some lies are ethical, while also claiming that "honesty is the best policy." In this article, we introduce a theory to explain this apparent inconsistency. Even though people view prosocial lies as ethical, they believe it is more important-and more moral-to avoid harmful lies than to allow prosocial lies. Unconditional honesty (simply telling the truth, without finding out how honesty will affect others) is therefore seen as ethical because it prevents the most unethical actions (i.e., harmful lies) from occurring, even though it does not optimize every moral decision. We test this theory across five focal experiments and 10 supplemental studies. Consistent with our account, we find that communicators who tell the truth without finding out how honesty will affect others are viewed as more ethical, and are trusted more, than communicators who look for information about the social consequences of honesty before communicating. However, the moral preference for unconditional honesty attenuates when it is certain that looking for more information will not lead to harmful lies. Overall, this research provides a holistic understanding of how people think about honesty and suggests that moral rules are not valued because people believe all rule violations are wrong, but rather, because they believe some violations must be avoided entirely. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".