Static AI Value Alignment Fails Under Capability Scaling
A new study published on arXiv suggests that relying solely on static, content-focused AI value alignment isn't enough as AI capabilities grow, distributions change, and autonomy increases. The researchers claim that any approach treating alignment as merely optimizing a fixed value—whether it's a reward function, utility function, guiding principles, or learned preferences—ends up stuck in what they call a "specification trap." They point out three philosophical issues that worsen this situation: Hume's is-ought gap, which shows that just because we see behavior doesn't mean we know what’s right; Berlin's value pluralism, which highlights the inconsistency in human values; and the extended frame problem, where value encoding might not suit future AI contexts. They note examples like RLHF and Constitutional AI as revealing deeper structural weaknesses, not just simple engineering problems. This study can be found on arXiv with ID 2512.03048.
Key facts
- Paper argues static AI value alignment is insufficient for robust alignment.
- Three philosophical results: Hume's is-ought gap, Berlin's value pluralism, extended frame problem.
- Critiques RLHF, Constitutional AI, inverse reinforcement learning, and cooperative assistance games.
- Failure modes are structural, not just engineering limitations.
- Published on arXiv with ID 2512.03048.
- Addresses capability scaling, distributional shift, and increasing autonomy.
- Any fixed formal value-object is insufficient.
- Specification trap is the central concept.
Entities
Institutions
- arXiv