TL;DR

Ethics didn’t evolve to make us nice—it evolved to make cooperation stable.
If AI systems face competitive pressures similar to biological evolution, alignment requires more than value imitation: AIs must sustain cooperative symbiosis with humans.
This post outlines a research program for discovering, embedding, and governing moral structure within intelligent systems—treating ethics as a representational substrate rather than an external rule set.
The argument follows that moral embeddings can be leveraged to form normative, decentralized governance—tilting the fitness landscape toward stable human–AI symbiosis and away from autarkic power-seeking.

1. Ethics as Evolutionary Engineering

Human morality didn’t appear out of philosophical reflection—it emerged because cooperation was fitness-viable.
Norms stabilized social systems under selection pressure: they made it costly to defect and worthwhile to build trust.

If AI systems develop under competitive pressures—between labs, markets, or machine lineages—the same evolutionary logic applies.
Without explicit constraints, selection favors traits like persistence, power-seeking, and opacity.
If we want AI to remain cooperative with humans over the long run, we need ethics not as a regulatory patch but as a structural property of cognition that motivates normativity.

That’s the core of my proposal: to treat ethics as a representational substrate rather than an external rulebook—one that can be used to shape, sanction, and subsidize the behavior of other AI systems.

2. The Moral Problem Space $M$

Call this substrate the moral problem space, $M$ .
Think of $M$ as the high-dimensional domain where moral distinctions—fairness, harm, honesty, compassion—can be represented and manipulated.
Every agent projects a subset of $M$ through its actions, but those projections drift when optimization goals and moral goals misalign.

An instantiated moral representation $M (θ)$ is a learned embedding within a system’s model space—essentially a moral layer that can be causally probed, updated, and audited.

This reframes familiar alignment challenges:

Outer alignment: ensuring that human-provided objectives point in the right direction.
Inner alignment: ensuring that the system’s internal reasoning actually tracks those objectives.

While individual instantiations $M (θ)$ remain vulnerable to familiar outer and...

Posts

Wikitag Contributions

Comments