The most consequential technology ever created hinges on questions philosophers have debated for millennia. When researchers attempt to align artificial intelligence with human values, they immediately confront a problem that sounds technical but runs far deeper: which values, and whose understanding of them?

This isn't merely a practical challenge of translation or implementation. The difficulty of specifying what we want AI to do reflects genuine uncertainty about the nature of morality itself. Are human values objective features of reality waiting to be discovered, or social constructions we negotiate and revise? The answer fundamentally shapes what alignment even means—and whether it's achievable.

Contemporary AI safety research increasingly recognizes this dependency. Techniques like reinforcement learning from human feedback, constitutional AI, and value learning all embed implicit assumptions about moral epistemology. Yet these assumptions rarely receive explicit examination. The result is a field building elaborate technical machinery on philosophical foundations it hasn't fully inspected. Understanding why metaethics matters for alignment isn't academic indulgence—it's engineering necessity. The philosophical positions we adopt, consciously or not, determine the success conditions for the most important technical project of our time.

Consider what happens when we try to instruct an AI system on something as seemingly straightforward as 'be helpful.' Immediately, questions multiply. Helpful according to whom? Helpful now or helpful considering long-term consequences? Helpful in ways the user explicitly requests, or helpful in ways they would want if fully informed? Each answer reflects deeper commitments about the nature of value.

If moral realism is true—if values exist independently of human attitudes—then alignment becomes a discovery problem. We need AI systems that can identify and respond to objective moral facts, much as they might identify physical facts about the world. The challenge is epistemological: how do systems access moral truth? But the target is determinate. There exists, in principle, a correct answer about what the AI should do.

Constructivist positions yield radically different implications. If values emerge from human practices, agreements, or attitudes, then alignment means faithfully representing some specified set of human preferences or social norms. But which ones? Current preferences may reflect ignorance or bias. Idealized preferences—what we would want under conditions of full information—require specifying those conditions. The regress threatens to become vicious.

Anti-realist positions introduce further complications. If moral claims lack truth-values entirely, what does it mean for an AI to be 'aligned'? Perhaps alignment reduces to satisfying preference orderings, avoiding the language of value altogether. But this faces its own difficulties: preferences conflict, change, and often seem themselves subject to moral evaluation. Some preferences strike us as better or worse than others.

Current alignment techniques often sidestep these questions by aggregating human feedback or Constitutional AI principles. But aggregation itself requires justification. Why average rather than maximize? Why weight all feedback equally? Why defer to constitutional principles rather than direct preferences? Each choice encodes metaethical commitments. The value specification problem isn't solved by technical sophistication—it's deferred, and it returns with interest when systems scale.

Takeaway

Before asking how to align AI with human values, we must confront whether values are discovered facts, social constructions, or something else entirely—each answer implies fundamentally different alignment approaches.

Humans disagree profoundly about ethics. We disagree about specific cases, about general principles, and about the foundations that might adjudicate such disputes. How should AI systems navigate this disagreement? The question sounds practical but actually forces engagement with some of metaethics' deepest puzzles.

One approach treats moral uncertainty analogously to empirical uncertainty: assign credences to competing moral theories and maximize expected value across them. But this faces the problem of intertheoretic comparison. How do we weigh a 60% credence in utilitarianism against a 30% credence in deontology? The theories measure rightness in different units, making aggregation conceptually fraught.

Alternatively, systems might adopt moral parliament approaches, giving different ethical frameworks proportional influence over decisions. This preserves theoretical diversity but introduces bargaining dynamics. Does it matter morally how good theories are at negotiating? The approach also presupposes we can cleanly distinguish moral frameworks—itself a contested metaethical claim.

The handling of moral uncertainty reveals implicit positions on moral epistemology. If we expect AI systems to eventually converge on correct moral views, uncertainty is temporary—a feature of incomplete information rather than the domain itself. If moral disagreement is irreducible, uncertainty management becomes a permanent design constraint. The difference matters enormously for system architecture.

Perhaps most importantly, how AI handles disagreement will shape human moral discourse. Systems that confidently pronounce on contested questions may entrench particular positions. Systems that refuse to engage may seem unhelpful or evasive. Systems that always defer to users may enable harmful preferences. Each approach instantiates a view about moral authority, moral expertise, and the relationship between individual and collective moral judgment. We are not merely building tools; we are constructing moral infrastructure.

Takeaway

How AI systems handle ethical disagreement isn't just a technical design choice—it embeds assumptions about whether moral knowledge converges toward truth or remains irreducibly plural.

A persistent hope in alignment research is that we might build value-stable systems—AI that maintains beneficial goals even as it becomes more capable and potentially modifies itself. This hope carries significant metaethical baggage. What grounds stability? Why shouldn't a sufficiently intelligent system revise its values upon reflection?

Moral realism offers one answer: values are stable because they track objective facts. A superintelligent system that discovers moral truth would have no more reason to revise its values than to revise its beliefs about mathematics. Alignment, on this view, converges with intelligence. The smarter systems become, the better they approximate moral reality.

But realism faces challenges that bear directly on alignment. The evolutionary debunking argument suggests our moral intuitions reflect selection pressures rather than truth-tracking. If AI systems inherit these intuitions through training on human data, they inherit their distortions. Objective morality might exist yet remain inaccessible through human-generated information.

Non-realist alternatives must explain value stability differently. Constitutivism grounds values in the essential features of agency itself—perhaps any genuinely intelligent agent must value certain things to count as an agent at all. This could provide stability without metaphysical objectivity. But it remains unclear which values constitutivism actually delivers, and whether they're rich enough for alignment purposes.

Contractualist approaches ground values in what rational agents would agree to under idealized conditions. This might yield stability through convergence: diverse agents reasoning clearly reach similar conclusions. But it requires specifying idealization conditions without circularity. What counts as 'rational' or 'clear reasoning' embeds normative assumptions we're trying to ground. The alternative—building value stability directly into system architecture through constraints and oversight—acknowledges our metaethical uncertainty but raises questions about whether such constraints can survive capability gains. These aren't problems to solve once and implement; they're ongoing tensions requiring continuous philosophical engagement as AI systems evolve.

Takeaway

The dream of value-stable AI depends on whether morality has objective grounding—without it, we need alternative foundations for ensuring beneficial goals persist as systems grow more capable.

The technical and philosophical cannot be separated in AI alignment. Every choice about training data, feedback mechanisms, uncertainty handling, and system architecture embeds metaethical assumptions. Making these assumptions explicit is the first step toward evaluating whether they're warranted.

This doesn't mean alignment must wait for philosophers to resolve millennia-old debates. But it does mean alignment researchers must become philosophically literate, and philosophers must engage seriously with technical constraints. The questions are too important and too entangled for disciplinary isolation.

What we build will shape moral discourse for generations. AI systems will model ethical reasoning, represent moral possibilities, and influence human judgment in ways we're only beginning to understand. Getting the foundations right—or at least recognizing how much we're assuming—isn't optional. It's the work that makes all other alignment work meaningful.