Informing a Robot Ethics Architecture Through Folk and Expert Morality
INFORMING A ROBOT ETHICS ARCHITECTURE THROUGH FOLK AND EXPERT MORALITY
This conference paper investigates how regular adults and ethics experts approach ethical decisions regarding deception in two scenarios: healthcare pill sorting and a child's board game. The authors collected survey data from 100 non-experts and 24 ethics experts evaluating various ethical frameworks, such as Utilitarianism, Kantian Ethics, and Virtue Ethics. The findings suggest that different scenarios prompt the use of specific ethical frameworks, providing empirical foundations for developing a multi-framework ethical architecture for autonomous robots.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
AI researchers and roboticists seeking computational approaches to robot ethics and human moral reasoning.
What the document contains
The paper covers empirical survey data collection comparing folk and expert moral evaluations of deception in two defined scenarios: assisting an older adult with pill sorting and playing a board game with a child. It analyses how different scenarios and features (such as task success, emotional state, and rule violations) correspond to established ethical frameworks.
The document does not provide deployed software code, formal legal regulations, or a fully implemented operational robot architecture.
- No single ethical framework is sufficient to capture the diversity of human ethical decision-making.
- Folk responses regarding pill sorting predominantly aligned with Utilitarian frameworks, focusing on practical patient outcomes.
- Folk responses regarding game playing and rule-breaking aligned with Virtue Ethics and Kantian Ethics, emphasizing social norms and emotional development.
- The empirical data collected from non-experts and experts are intended to inform a dynamic, multi-framework ethical architecture for robots.
· 1. Introduction · 2. Related Work · 3. Folk and Expert Survey Data Collection · 4. Folk and Expert Survey Results · 5. Folk Morality Survey Open Response Analysis · 6. Discussion · 7. Conclusions
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
N/A
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
N/A
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
Sorting one’s own pills increases autonomy and is a common, and vitally important, motor exercise for patients with Parkinson’s disease.
1. Introduction
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
N/A
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
N/A
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
N/A
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
N/A
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
N/A
screening score · 2026-10-23