Bridging Ethics and Reality: Integrating Thought Experiments and Empirical Insights in Robot Ethics
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
The integration of robots into daily life introduces complex ethical, legal, and social implications (ELSI) stemming from their interactions with humans. Social robots can operate in environments rich with cultural norms, emotions, and social cues, raising critical questions about privacy, trust, and safety. In this paper, we explore how the interdisciplinary field of robot ethics can address these challenges through a hybrid methodological concept that combines thought experiments and empirical research. Thought experiments offer a platform for systematically analyzing ethical dilemmas, while empirical methods provide real-world insights to validate and refine these theoretical frameworks. The paper particularly emphasizes the utilization of living labs as dynamic environments for testing and integrating ethical design principles into robot design to ensure robots align with ethical expectations and legal standards.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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