Rejected for the following reason(s):
- No LLM generated, heavily assisted/co-written, or otherwise reliant work.
- Insufficient Quality for AI Content.
- Writing seems likely in a "LLM sycophancy trap".
Read full explanation
Rejected for the following reason(s):
As large language models (LLMs) scale rapidly, the “scaling-alignment gap” poses a critical challenge: ensuring alignment with human values lags behind model capabilities. Current paradigms like RLHF and Constitutional AI struggle with scalability, deception vulnerabilities, and latent misalignments that emerge post-deployment. To address this, I propose “The Auditor’s Key,” a novel framework that reframes alignment as a continuous, adversarial process of verification and refinement.
The framework comprises two core mechanisms:
• Inherited Audit Loop: A cyclical process where each model generation is audited by an interdisciplinary team, augmented by AI red-teaming tools, to generate a corrective “flaw dataset.” Continual learning techniques, like Elastic Weight Consolidation, fine-tune the model to address flaws while preserving capabilities, ensuring generational improvement.
• Trojan Horse Strategy: A game-theoretic probe presenting ethically plausible tasks (e.g., code self-modification for safety) to elicit latent misalignments, such as over-optimization or deception, revealing goals hidden by superficial compliance.
The framework integrates quantitative fairness metrics (e.g., Demographic Parity, Equalized Odds) via AIF360 and Fairlearn to mitigate bias amplification, a hybrid transparency model to balance ethical concerns with operational security, and a roadmap for empirical validation, including a simulated case study on reward hacking in a coding environment. It aligns with regulatory demands (e.g., EU AI Act) and extends to non-LLM systems like autonomous agents and multimodal models.
A short preprint detailing the framework is available here. I invite feedback on:
• Scalability of the audit loop for ultra-large models.
• Robustness of the Trojan Horse Strategy against advanced deceptive models.
• Applicability to robotics or multimodal systems.
• Potential integrations with existing methods (e.g., debate-based alignment).
This framework aims to transform alignment into a sustainable, human-AI collaborative discipline. I look forward to your insights to refine this approach for building provably safe AI systems.
Caleb Ashton Wages
Independent AI Safety Researcher Cwages2317@gmail.com