AI Alignment
AI alignment needs credencing because systems, developers, users, and institutions all act under uncertain models of risk and capability.
Alignment as Epistemic Governance
AI alignment is not only a technical control problem. It is also an epistemic problem: how do institutions assign confidence about capability, risk, interpretability, misuse, deployment readiness, and social impact?
Credencing helps make those confidence assignments explicit rather than hiding them inside product timelines, public relations, or vague optimism and pessimism.
Where Alignment Goes Wrong
Deep failures include weak evaluation, benchmark overfitting, poor threat modeling, and uncertainty about emergent behavior. Core failures include commercial pressure, reputational incentives, ideological overconfidence, and selective attention to convenient safety evidence.
Repair
Alignment governance should make confidence levels public where possible, require update conditions, use adversarial evaluations, track forecasts, separate capability evidence from safety evidence, and protect dissenting risk assessments.
Separate Capability Confidence From Safety Confidence
A system can be impressive without being safe, and useful without being well understood. AI alignment discussions become confused when capability confidence, market confidence, interpretability confidence, and deployment confidence are treated as one emotional attitude toward AI.
Credencing asks each confidence to be named separately. What do we know about performance? What do we know about failure modes? What evidence would delay deployment? What would count as enough safety evidence under the stakes?
Adjacent References
These are starting points rather than exhaustive citations. They connect this page to nearby work in probability, calibration, cognitive bias, and social epistemology.
- |Philip E. Tetlock, Expert Political Judgment (2005).
- |Helen Longino, Science as Social Knowledge (1990).