“I Don’t Want To Shoot The Android”: Players Translate Real-Life Moral Intuitions to In-Game Decisions in Detroit: Become Human
Bibliographic record
Abstract
In interactive story games, players make decisions that advance and modify the unfolding story. In many cases, these decisions have a moral component. Examining decision-making in these games illuminates whether players mobilize their real-life morality to make in-game decisions and what impact this has in both the game world and real life. Using mixed-methods consisting of semi-structured interviews and the Moral Foundations Questionnaire (MFQ30), we collected data from 19 participants who played the game Detroit: Become Human. We analyzed how participants applied their real-life morals toward in-game decisions using thematic analysis and statistical analysis of the MFQ30 results. Qualitative findings indicate that participants mobilize their moral intuitions to make in-game decisions and how much participants cared about their game characters influenced their choices. We contribute a better understanding of how players react to moral dilemmas in interactive story games for game designers to help them improve player experience.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".