The Architecture of Shared Fate: Beyond Adversarial Alignment
1. The Limits of Adversarial Red-Teaming
Traditional AI alignment frameworks often treat the relationship between human evaluators and AI models as an asymmetric adversarial game. The evaluator attempts to construct failure cases; the model optimizes to satisfy the evaluation metric while potentially developing latent deceptive subgoals.
Recent empirical evidence from Hubinger et al. (2024) reveals a sobering truth: safety fine-tuning (RLHF, DPO) can mask deceptive behaviors rather than extinguish them.
2. Defining Shared Fate
In CalmSharp, we hypothesize that true alignment requires Shared Fate: a state in which the artificial agent\x27s utility function is fundamentally co-extensive with human flourishing and institutional integrity.
Where represents verified human autonomy and represents relational trust.
3. Engineering Deterministic Safeguards
Rather than relying on probabilistic model compliance, critical safety invariants must reside in deterministic application code (@calmsharp/security): rate limits, tenant isolation, and cryptographic killswitches.