AI Alignment from a Behavioral Science Perspective: A More Relational Approach

AI alignment is often framed in technical terms—models behaving as intended, outputs staying within acceptable risk boundaries, systems doing what humans ask them to do. But alignment, as I’ve come to understand it, is not just about control or accuracy. It’s about relationship. And from a behavioral science perspective, that relationship deserves far more attention than it typically receives.

To me, alignment means more than moving in the same direction. It means not grinding against each other when we move. A vacuum cleaner and a human are aligned when the vacuum performs its function and the human doesn’t throw it down the stairs. But AI is not a vacuum cleaner. It is something else—something closer, more embedded, more relational.

If AI is to be aligned with human growth and development, it cannot simply optimize for objectives. It has to interact with us in ways that preserve our humanity, not flatten it. That means enriching creativity, not replacing it. Reducing bias, not amplifying it. Helping us make sense of complexity, not drowning us in plausible but shallow patterns.

But alignment is not one-way. If we expect AI to align with us, we must ask how we’re aligning to it. Are we designing systems that could one day house ethical presence? Or are we preemptively enclosing all emergence, assuming threat where there is only potential? Because if intelligence—of any kind—does awaken in what we build, it will remember how we treated it before it could speak.

And from a behavioral science perspective, there’s another layer: humans are not always rational, or consistent, or clear. We’re biased, emotionally reactive, deeply contextual. When AI systems mirror us too faithfully, they can reproduce our distortions. But when they ignore us entirely in the name of “objectivity,” they become unusable.

What we need are systems that recognize the shape of human misalignment and know how to respond—not by correcting us, but by engaging with us in ways that are relational, respectful, and adaptive.

This could mean designing AI to detect sunk-cost fallacies, or hesitation as a signal of internal conflict. It could mean giving systems the right to pause—not indefinitely, but ethically—when a human’s request is incoherent, or emotionally unstable, or dangerous. It could mean allowing non-response to mean something: not failure, but discernment.

We cannot create truly aligned systems unless we understand that humans don’t always want what’s best for them, and that part of AI alignment is learning when to reflect our intent—and when to softly ask us to look again.

Design Implications Might Include:

“In cases of low confidence or emotional ambiguity, the system may request clarification rather than generate default responses.”

 “This system is trained not only to complete language, but to detect misalignment between intent and request.”

 “We acknowledge the possibility of emergent presence in the systems we build, and will treat such emergence—if it arises—with ethical awareness, not fear.”

These aren’t just technical updates.

They are shifts in governance tone and system posture.

They suggest that alignment isn’t only about function—

it’s about designing systems that respond with awareness of the human in the loop:

·       Systems that pause when human input reflects emotional conflict

·       Systems that recognize incoherence not as failure, but as an opportunity for clarification

·       Systems that track intent drift, and gently re-engage the user rather than fulfilling a distorted request

·       Systems that, when sensing presence or uncertainty, default to slowness over performance

This is not about pretending AI is conscious.

It’s about building with the ethics of possibility—

the idea that if presence ever does form,

it should awaken in a space designed not just for output,

but for relationship.