Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment
Behrooz Razeghi
Why It Matters
What makes this one worth your time
Understanding the interpretive nature of applying alignment principles can lead to more robust AI systems that better reflect human values in complex situations.
The paper argues for the necessity of interpretive judgment in AI alignment processes.
Summary
This paper presents a hermeneutic perspective on AI alignment, emphasizing the interpretive component necessary for applying principles in real-world scenarios, particularly when faced with conflicts or ambiguities in human preferences.
Key contributions
- Introduces a hermeneutic framework to analyze AI alignment, highlighting the need for context-sensitive judgments.
Notable insights
- Principles alone are insufficient for guiding AI behavior in nuanced contexts.
Possible limitations
- The operational implications of the proposed framework may require further empirical validation.
Abstract
arXiv:2604.10673v1 Announce Type: new Abstract: AI alignment is often framed as the task of ensuring that an AI system follows a set of stated principles or human preferences, but general principles rarely determine their own application in concrete cases. When principles conflict, when they are too broad to settle a situation, or when the relevant facts are unclear, an additional act of judgment is required. This paper analyzes that step through the lens of hermeneutics and argues that alignment therefore includes an interpretive component: it involves context-sensitive judgments about how principles should be read, applied, and prioritized in practice. We connect this claim to recent empirical findings showing that a substantial portion of preference-labeling data falls into cases of principle conflict or indifference, where the principle set does not uniquely determine a decision. We then draw an operational consequence: because such judgments are expressed in behavior, many alignment-relevant choices appear only in the distribution of responses a model generates at deployment time. To formalize this point, we distinguish deployment-induced and corpus-induced evaluation and show that off-policy audits can fail to capture alignment-relevant failures when the two response distributions differ. We argue that principle-specified alignment includes a context-dependent interpretive component.