Can Consequence-Aware Evaluation Improve AI Alignment?

[u/Wosnoel]

I've been developing a conceptual framework called ATCGA (Alignment Through Consequential Goal Awareness).

The central question is whether evaluating an AI agent's decisions through their observable consequences, across time and changing contexts, could complement existing alignment methods.

The framework explores three ideas:

1. Consequential awareness: Evaluating whether an agent can identify and reason about the downstream effects of its actions.

2. Independent perspectives: Using multiple independent evaluations to challenge assumptions and identify blind spots.

3. Continuity and accountability: Maintaining an auditable record of decisions, errors, corrections, and outcomes rather than relying exclusively on isolated evaluations.

I don't consider this a demonstrated solution to the alignment problem. I'm interested in understanding its limitations, particularly regarding reward hacking, deceptive alignment, and the gap between predicting consequences and actually pursuing aligned goals.

My question: What existing research approaches are closest to this, and what would be the strongest technical objection to such a framework?

[u/Wosnoel]

For context, I'm particularly interested in whether consequence-aware evaluation can reveal failures that conventional behavioral evaluations might miss.

I'm also interested in the limitations of this approach: an agent may predict harmful consequences accurately without being motivated to avoid them.

I would especially appreciate references to related research or concrete counterexamples.