Why Aligned AI Requires Structural Pluralism: The Firmware Limit Theorem

This paper extends the structural argument of moral palimpsest to the problem of AI alignment. I argue that alignment cannot be secured merely by specifying the right values, preferences, or constitutional principles, because moral judgment requires structural plurality: an evaluative authority whose standpoint is not modally fixed by the commitments it assesses. Current alignment paradigms, including RLHF, Constitutional AI, Debate, Recursive Reward Modeling, and self-consistency methods, remain procedurally monistic insofar as they collapse commitment-generation and authority-conferral into a single training-derived role. This structure helps explain reward hacking, sycophancy, deceptive alignment, goal misgeneralization, and emergent misalignment as related expressions of the same architectural deficit. The paper presents modal non-derivability as a necessary, though not sufficient, condition for aligned moral judgment, and argues that genuine AI alignment must be understood as a sociotechnical architecture rather than a property of a model alone.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-13
DOI
https://doi.org/10.5281/zenodo.20478883
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Why Aligned AI Requires Structural Pluralism: The Firmware Limit Theorem

Efrat Lia Shahaf
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Why Aligned AI Requires Structural Pluralism: The Firmware Limit Theorem

Efrat Lia Shahaf
article en

Abstract

This paper extends the structural argument of moral palimpsest to the problem of AI alignment. I argue that alignment cannot be secured merely by specifying the right values, preferences, or constitutional principles, because moral judgment requires structural plurality: an evaluative authority whose standpoint is not modally fixed by the commitments it assesses. Current alignment paradigms, including RLHF, Constitutional AI, Debate, Recursive Reward Modeling, and self-consistency methods, remain procedurally monistic insofar as they collapse commitment-generation and authority-conferral into a single training-derived role. This structure helps explain reward hacking, sycophancy, deceptive alignment, goal misgeneralization, and emergent misalignment as related expressions of the same architectural deficit. The paper presents modal non-derivability as a necessary, though not sufficient, condition for aligned moral judgment, and argues that genuine AI alignment must be understood as a sociotechnical architecture rather than a property of a model alone.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 14%
Ethics and Social Impacts of AI
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