Epistemic Norms for AI Safety and Alignment Research
Keivan Navaie
Why It Matters
What makes this one worth your time
AI engineers and researchers should care because ensuring the safety and alignment of AI systems is crucial to prevent catastrophic failures, and this paper proposes a framework to improve the reliability of safety-related research.
ECAISA proposes a structured framework for documenting and verifying AI safety research claims.
Summary
The paper argues for the need to establish distinct epistemic norms for AI safety and alignment research, contrasting it with mainstream AI research. It introduces the ECAISA framework, which includes principles and mechanisms for documenting and verifying safety-relevant research claims, aiming for auditability rather than certification.
Key contributions
- Introduction of the ECAISA framework for AI safety and alignment.
- Identification of five gap dimensions in current alignment research.
- Proposal of a structured synthesis grounded in a preregistered bibliometric baseline.
Notable insights
- The paper identifies a gap in institutionalized independent verification in AI alignment research.
- It proposes a multi-tiered framework that balances transparency with information hazards and commercial confidentiality.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.24243v1 Announce Type: new Abstract: Mainstream AI research emphasises capability growth and tolerates low failure rates when average-case performance is high. AI safety and alignment research has a different mission: to ensure that catastrophic failures never occur, under sparse evidence, adversarial dynamics, and fat-tailed risk. We argue that the two domains differ along two analytically independent axes---{\it capability profile}, demonstrating the absence of hazardous behaviours rather than the presence of positive capabilities, and {\it risk profile}, bounding worst-case outcomes under fat-tailed uncertainty rather than optimising average-case performance---and that mainstream epistemic practices are inadequate on both. Building on a structured synthesis grounded in a preregistered bibliometric baseline, we identify five cross-cutting gap dimensions in current alignment research, including the near-absence of institutionalised independent verification. To address these gaps, we propose {\sc ECAISA}, an Epistemic Code for AI Safety and Alignment comprising eight principles, a three-level scoring rubric, a four-level disclosure ladder that reconciles transparency with information-hazard and commercial-confidentiality constraints, a tiered applicability scheme, an information-hazard adjudication procedure, and seven anti-gaming mechanisms. {\sc ECAISA} does not certify that any AI system is safe; it constrains how safety-relevant research claims are documented, checked, and relied upon, with auditability rather than certification as its governance target.