Attributions of intent and moral responsibility to AI agents
Bibliographic record
Abstract
Moral transactions are increasingly infused with decision input from AI agents. To what extent do observers believe that AI agents are responsible for their own actions? How do these AI agents' socio-psychological features affect observers' judgment of them when they transgress? With full factorial, between-participant designs, we presented participants with vignettes in which an AI agent contributed to a negative outcome either intentionally or unintentionally. We independently manipulated four features of the agent's mind: its adherence to moral values, autonomy, emotional self-awareness, and social connectedness. In Study 1 ( N = 2012), AI agents that intentionally contributed to a negative outcome consistently received harsher judgments than AI agents that contributed unintentionally. For unintentional actions, socially connected AI agents received less harsh judgments than socially disconnected AI agents. In Studies 2a-c ( N = 1507), these judgments were explained by ratings of the socially connected AI agent's ‘mind’ as less distinct from the mind of its programmers (Study 2b) and that this kind of agent also possessed less free will (Study 2c). We discuss the implications of these findings in advancing the field's understanding of the moral psychology—and design—of AI agents. • Moral judgments of AI agents are sensitive to the agents' manipulated intentionality. • AI agents contributing intentionally to negative outcomes received harsher judgments. • AI agents embedded in a human social network received more lenient judgments. • Socially connected AI agents are rated as possessing less free will. • Socially connected AI agents' ‘minds’ are rated as less distinct entities.
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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 | 0.008 | 0.083 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".