Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment

2026-04-12

AI Summary

This study addresses the challenge that alignment principles in artificial intelligence are often difficult to apply automatically due to contextual ambiguity, conflicting norms, or factual uncertainty, and that existing static evaluation methods fail to capture the dynamic judgments required during deployment. Drawing on philosophical hermeneutics, the paper argues that alignment inherently involves context-dependent interpretive processes that necessitate situated interpretation, balancing, and prioritization of principles. By distinguishing between deployment-induced and corpus-induced evaluations and conducting empirical analysis on preference annotation data, the work reveals the limitations of off-policy auditing and demonstrates that many alignment failures manifest only within the model’s actual behavioral distribution—rendering them invisible to conventional assessment approaches. This research thus introduces a novel framework for dynamic, context-sensitive alignment evaluation.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• AI alignment
• hermeneutics
• principle application
• context-sensitive judgment
• interpretive component

Innovation

• hermeneutics
• AI alignment
• interpretive judgment
• principle conflict
• deployment-induced evaluation