Case-Based Robotic Architecture with Multiple Underlying Ethical Frameworks for Human-Robot Interaction
CASE-BASED ROBOTIC ARCHITECTURE WITH MULTIPLE UNDERLYING ETHICAL FRAMEWORKS FOR HUMAN-ROBOT INTERACTION
This conference paper introduces a flexible case-based robotic architecture designed to guide ethical decision-making in human-robot interactions by leveraging cases from multiple ethical frameworks. The architecture selects appropriate robotic actions based on the specific situation and user context, using survey data derived from both folk morality and formal ethical frameworks. The design is implemented and illustrated through a pill-sorting scenario involving an older adult, where the robot decides whether to provide false encouragement or an accurate assessment.
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 interested in implementing ethical decision-making frameworks in autonomous systems.
What the document contains
The paper presents a robotic architecture utilizing case-based reasoning to handle ethical dilemmas in human-robot interaction. It covers the incorporation of diverse ethical frameworks, survey-based case generation, and an implementation involving a pill-sorting scenario with older adults.
The document lacks legally binding regulatory standards, formal compliance mechanisms, and enforcement guidelines for commercial deployment.
- A case-based reasoning robotic architecture can integrate multiple ethical frameworks to guide human-robot interactions.
- Cases within the architecture are populated using survey data representing both folk morality and formal ethical theories.
- The architecture selects robotic actions dynamically based on features of the current situation, such as user performance and emotional state.
- An implementation scenario explores whether a robot should use deceptive false encouragement during a pill-sorting task to maintain user engagement.
· 1. Introduction · 2. Background · 3. Architecture Design · 4. Architecture Implementation · 5. Discussion · 6. Conclusion
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
screening score · 2026-10-23
Fairness
1/5Non-discrimination and equitable treatment
Social Justice Theory [19]
4. Architecture Implementation
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
older adults with memory issues and training of the task can lead to frustration
4. Architecture Implementation
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-23