Improving Corrigibility in LLMs. Also interested in AI Control and using AI to differentially accelerate AI Safety.
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I believe a recursively aligned AI model would be more aligned and safe than a corrigible model, although both would be susceptible to misuse.
Why do you disagree with the above statement?
Thanks for the clarification, this makes sense! The key is the tradeoff with corrigibility.
Thanks, updated the comment to be more accurate
If you ask a corrigible agent to bring you a cup of coffee, it should confirm that you want a hot cup of simple, black coffee, then internally check to make sure that the cup won’t burn you, that nobody will be upset at the coffee being moved or consumed, that the coffee won’t be spilled, and so on. But it will also, after performing these checks, simply do what’s instructed. A corrigible agent’s actions should be straightforward, easy to reverse and abort, plainly visible, and comprehensible to a human who takes time to think about them. Corrigible agents proactively study themselves, honestly report their own thoughts, and point out ways in which they may have been poorly designed. A corrigible agent responds quickly and eagerly to corrections, and shuts itself down without protest when asked. Furthermore, small flaws and mistakes when building such an agent shouldn’t cause these behaviors to disappear, but rather the agent should gravitate towards an obvious, simple reference-point.
Isn't corrigibility still susceptible to power-seeking according to this definition? It wants to bring you a cup of coffee, it notices the chances of spillage are reduced if it has access to more coffee, so it becomes a coffee maximizer as in instrumental goal.
Now, it is still corrigible, it does not hide it's thought processes, it tells the human exactly what it is doing and why. But when the agent is doing millions of decisions and humans can only review so many thought processes (only so many humans will take the time to think about the agent's actions), many decisions will fall through the crack and end up being misaligned.
Is the goal to learn the human's preferences through interaction then, and hope that it learns the preferences enough to know that power-seeking (and other harmful behaviors) are bad?
The problem is, there could be harmful behaviors we haven't thought of to train the AI in, and they are never corrected, so the AI proceeds with them.
If so, can we define a corrigible agent that is actually what we want?
How does corrigibility relate to recursive alignment? It seems like recursive alignment is also a good attractor - is it that you believe it is less tractable?
What assumptions do you disagree with?
Thanks for this insightful post! It clearly articulates a crucial point: focusing on specific failure modes like spite offers a potentially tractable path for reducing catastrophic risks, complementing broader alignment efforts.
You're right that interventions targeting spite – such as modifying training data (e.g., filtering human feedback exhibiting excessive retribution or outgroup animosity) or shaping interactions/reward structures (e.g., avoiding selection based purely on relative performance in multi-agent environments, as discussed in the post) – aim directly at reducing the intrinsic motivation for agents to engage in harmful behaviors. This isn't just about reducing generic competition; it's about decreasing the likelihood that an agent values frustrating others' preferences, potentially leading to costly conflict.
Further exploration in this area could draw on research in:
- Multi-Agent Reinforcement Learning (MARL) in Sequential Social Dilemmas
- Open Problems in Cooperative AI
Focusing on reducing specific negative motivations like spite seems like a pragmatic and potentially high-impact approach within the broader AI safety landscape.
Appreciate the insights on how to maximize leveraged activities.
With the planning fallacy making it very difficult to predict engineering timelines, how do top performers / managers create effective schedules and track progress against the schedule?
I get the feeling that you are suggesting to create a Gantt chart, but from your experience, what practices do teams use to maximize progress in a project?
Based on previous data, it's plausible like CCP AGI will perform worse on safety benchmarks than US AGI. Take Cisco Harmbench evaluation results:
- DeepSeek R1: Demonstrated a 100% failure rate in blocking harmful prompts according to Anthropic's safety tests.
- OpenAI GPT-4o: Showed an 86% failure rate in the same tests, indicating better but still concerning gaps in safety measures.
- Meta Llama-3.1-405B: Had a 96% failure rate, performing slightly better than DeepSeek but worse than OpenAI.
Though, if it was just CCP making AGI or just US making AGI it might be better because it'd reduce competitive pressures.
But, due to competitive pressures and investments like Stargate, the AGI timeline is accelerated, and the first AGI model may not perform well on safety benchmarks.
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Reading Resources for Technical AI Safety independent researchers upskilling to apply to roles:
Application and Upskilling resources;