Reproduce “Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principles” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yue Huang, Chujie Gao, Yujun Zhou, Kehan Guo, Xiangqi Wang, Or Cohen-Sasson, Max Lamparth, Xiangliang Zhang
To address the limitations of static ethical guidelines in AI, a systematic reference framework is proposed for an adaptive interpretation of the Helpful, Honest, and Harmless (HHH) principle. This framework enables the operationalization of HHH by providing a structured methodology for balancing its dimensions across diverse contexts and managing inherent trade-offs in AI development and governance.
Key insights
- The HHH principles serve as ethically grounded objectives, but their effective operationalization requires systematic contextualization rather than static application.
- Harmlessness can function as a foundational safeguard, often synergistically boosting overall helpfulness rather than existing merely as a limiting constraint.
- High-risk evaluations reveal critical interdependencies among HHH dimensions, where independent assessment is insufficient and risks underestimating systemic hazards.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.