Moral decision making: Explainable insights into the role of working memory in autonomous driving
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
• Working memory load impedes utilitarianism under time pressure. • Gaussian Naive Bayes model predicts ethical decisions with up to 97 % accuracy. • 2-second decision window optimal for balancing time constraints and working memory. • Partial Dependence Plots show strong negative correlation between workload and moral choices. • Findings inform development of ethically-aligned AI systems under cognitive load. The intersection of Artificial Intelligence (AI) and moral philosophy presents unique challenges in the development of autonomous vehicles, particularly in scenarios requiring split-second ethical decisions. This study examines the relationship between working memory (WM) and moral judgments in simulated AV scenarios, quantifying the effects of varying cognitive load on utilitarian decision-making under different time constraints. We experimented with 336 participants, each completing 16 simulated driving trials presenting unique ethical dilemmas. Results reveal a complex interplay between cognitive load and ethical choices. Under high temporal pressure (1-second response window), utilitarian decisions decreased significantly from 92.77 % to 70.08 %. Extended time constraints led to increased utilitarian choices. Statistical analyses validated these findings across diverse ethical contexts. Chi-square tests revealed significant associations between WM load and utilitarian decisions in 1-second conditions, particularly for high-stakes scenarios. Logistic regression showed that WM significantly decreased the likelihood of utilitarian decisions in these scenarios. Six supervised machine learning models were employed, with Gaussian Naive Bayes achieving the highest predictive accuracy (82.2 % to 97.0 %) in distinguishing utilitarian decisions. Partial Dependence analysis revealed a strong negative correlation between WM and utilitarian decisions, especially in the 1-second interval. The 2-second interval emerged as potentially optimal for balancing time constraints and cognitive load. These findings contribute to the theoretical understanding of ethical decision-making under cognitive load and provide practical insights for developing ethically aligned autonomous systems, with implications for improving safety, optimizing takeover protocols, and enhancing the ethical reasoning capabilities of autonomous driving systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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