The Moral Psychology of Conflicts of Interest: Insights from Affective Neuroscience
Bibliographic record
Abstract
abstract This paper is an investigation of the moral psychology of decisions that involve a conflict of interest. It draws on the burgeoning field of affective neuroscience, which is the study of the neurobiology of emotional systems in the brain. I show that a recent neurocomputational model of how the brain integrates cognitive and affective information in decision‐making can help to answer some important descriptive and normative questions about the moral psychology of conflicts of interest. These questions include: Why are decisions that involve conflicts of interest so common? Why are people so often unaware that they are acting immorally as the result of conflicts of interest? What is the relation of conflicts of interest to other kinds of irrationality, especially self‐deception and weakness of will? What psychological, social, and logical steps can be taken to reduce the occurrence of immoral decisions resulting from conflicts of interest? I discuss five strategies for dealing with conflicts of interest: avoidance, optimal reasoning patterns, disclosure, social oversight, and understanding of neuropsychological processes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".