Executive Summary: A Hybrid Approach to AI Alignment – Preventing Misalignment Risks (Full Paper Coming After 4pm Today)

Hi all, I've been working on a framework for tackling AI misalignment, building on current research like RLHF and Constitutional AI. This exec summary highlights the challenges, four key uncertainties (tractability, timelines, explosion type, coord feasibility), and actionable recommendations.

The Challenge: From today's issues (bias, misinformation) to existential risks if systems optimize misaligned goals.

Recommended Actions:

• Institutional: Tiered safety regs, fund priorities like interpretability/corrigibility, capability controls, int'l coord, address intermediate risks, explore non-agentic tools.
• Individual: Educate (AI Safety Fundamentals/LessWrong), donate (CAIS/Anthropic/EA funds), advocate evidence-based policy, career shifts, responsible practices.

Technical Approach: Swiss cheese layers – value learning, architectural constraints, oversight, checkpoints, graduated coord.

Success Metrics & Timeline: Track deceptive behavior reductions, aligned persistence; priorities from now (2025) to mid-term (2030-2040).

What won't help: Doom-posting, anthropomorphizing, blanket restrictions.

This is a starting point—honest about unknowns. Full paper after 4pm today. Feedback welcome: Which layer seems most promising? Or weakest?

Resources: https://drive.google.com/file/d/1E8LlFxyJ2tN0idEWBa12Y30tN2r0eYTy/view?usp=sharing

Looking forward to discussion!