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

TL;DR

This paper advocates for an adaptive approach to the Helpful, Honest, and Harmless (HHH) principle in AI alignment, emphasizing context-aware prioritization and a structured framework to balance conflicting values across diverse scenarios.

Contribution

It introduces a priority-based framework for adapting the HHH principle, addressing ambiguities and conflicts through context definition, value prioritization, and risk assessment.

Findings

Identified ambiguities in HHH interpretation through case studies

Developed a structured priority order for balancing HHH dimensions

Proposed a comprehensive framework for context-aware AI alignment

Abstract

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.