# Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principles

arXiv: https://arxiv.org/abs/2502.06059 · HTML: https://mlsanity.com/paper/2502.06059/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [DICE: A Framework for Dimensional and Contextual Evaluation of Language Models](https://mlsanity.com/paper/2504.10359/) (2025)
- [Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations](https://mlsanity.com/paper/2604.27093/) (2026)
- [Compass-Thinker-7B Technical Report](https://mlsanity.com/paper/2508.08909/) (2025)
- [Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills](https://mlsanity.com/paper/2508.19500/) (2025)
- [Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning](https://mlsanity.com/paper/2607.29211/) (2026)

## Abstract (arXiv)

The Helpful, Honest, and Harmless (HHH) principle is a foundational framework for aligning AI systems with human values. However, existing interpretations of the HHH principle often overlook contextual variability and conflicting requirements across applications. In this paper, we argue for an adaptive interpretation of the HHH principle and propose a reference framework for its adaptation to diverse scenarios. We first examine the principle's foundational significance and identify ambiguities and conflicts through case studies of its dimensions. To address these challenges, we introduce the concept of priority order, which provides a structured approach for balancing trade-offs among helpfulness, honesty, and harmlessness. Further, we explore the interrelationships between these dimensions, demonstrating how harmlessness and helpfulness can be jointly enhanced and analyzing their interdependencies in high-risk evaluations. Building on these insights, we propose a reference framework that integrates context definition, value prioritization, risk assessment, and benchmarking standards to guide the adaptive application of the HHH principle. This work offers practical insights for improving AI alignment, ensuring that HHH principles remain both ethically grounded and operationally effective in real-world AI deployment.