Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction

The Flawed Foundation of Static Preferences

A new preprint on arXiv challenges a core assumption underpinning most current AI alignment research: that human preferences are stable, coherent targets waiting to be discovered and optimized. The paper, "Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction," argues instead that preferences are "layered, dynamic, and constructed through" interaction. This is not merely a theoretical quibble—it strikes at the heart of how we build reward models, fine-tune LLMs, and evaluate system safety.

What the Research Reveals

The authors synthesize extensive empirical evidence from psychology, behavioral economics, and human-computer interaction to demonstrate that preferences shift depending on context, framing, and the very act of elicitation. When an AI asks a user to rate a response, it is not measuring a pre-existing value—it is co-creating one. The paper proposes "constructive alignment" as an alternative framework, where alignment is not a one-time optimization problem but an ongoing governance process that accounts for preference fluidity.

This aligns with known phenomena: users often express contradictory preferences across different sessions, or change their stated values after seeing model outputs. Traditional RLHF (Reinforcement Learning from Human Feedback) treats these inconsistencies as noise to be averaged out. The new work suggests they are signal—evidence that preferences are being actively shaped by the interaction itself.

Why This Matters

If preferences are constructed, not discovered, then current alignment methods risk optimizing for a phantom. A reward model trained on static preference data may become misaligned the moment deployment context changes. More concerning, it could lock in transient or poorly considered preferences, effectively freezing a user's worst momentary judgment.

This has direct implications for safety. A system that treats preferences as fixed cannot distinguish between a user's genuine long-term values and a fleeting impulse. It cannot adapt when a user's understanding evolves through conversation. The paper's governance framing suggests we need mechanisms for preference revision, not just preference capture.

Implications for AI Practitioners

For those building and deploying AI systems, this research points to several practical shifts:

• Rethink evaluation metrics. Static benchmarks that assume stable preferences will miss misalignment that emerges dynamically. Practitioners should invest in longitudinal studies and interactive evaluation protocols.
• Design for preference articulation. Instead of asking users to rate outputs, systems could help users clarify and revise their own values through dialogue. This turns alignment from a measurement problem into a collaborative process.
• Build in preference governance. The paper implies that systems need explicit mechanisms for users to revisit, override, or evolve past preferences. This is more than a "reset settings" button—it requires tracking preference trajectories and flagging when a current request conflicts with previously expressed values.
• Prepare for regulatory scrutiny. As alignment becomes a policy focus, regulators may demand evidence that systems can handle preference change. Static alignment proofs will likely be insufficient.

Key Takeaways

• Human preferences are not fixed targets but are dynamically constructed through interaction with AI systems, challenging the foundation of most current alignment methods.
• Current RLHF and reward modeling approaches risk optimizing for transient or poorly considered preferences, potentially locking in misalignment.
• Practitioners should shift from static preference capture to ongoing preference governance, including mechanisms for revision and longitudinal evaluation.
• The paper reframes alignment as a collaborative, evolving process rather than a one-time optimization problem, with significant implications for safety and regulatory compliance.