Computing scientist and Systems architect. Currently doing self-funded AI/AGI safety research.  I participate in AI standardization under the company name Holtman Systems Research: https://holtmansystemsresearch.nl/


Counterfactual Planning

Wiki Contributions



I am not aware of any good map of the governance field.

What I notice is that EA, at least the blogging part of EA, tends to have a preference for talking directly to (people in) corporations when it comes to the topic of corporate governance. As far as I can see, FLI is the AI x-risk organisation most actively involved in talking to governments. But there are also a bunch of non-EA related governance orgs and think tanks talking about AI x-risk to governments. When it comes to a broader spectrum of AI risks, not just x-risk, there are a whole bunch of civil society organisations talking to governments about it, many of them with ties to, or an intellectual outlook based on, Internet and Digital civil rights activism.

As requested by Remmelt I'll make some comments on the track record of privacy advocates, and their relevance to alignment.

I did some active privacy advocacy in the context of the early Internet in the 1990s, and have been following the field ever since. Overall, my assessment is that the privacy advocacy/digital civil rights community has had both failures and successes. It has not succeeded (yet) in its aim to stop large companies and governments from having all your data. On the other hand, it has been more successful in its policy advocacy towards limiting what large companies and governments are actually allowed to do with all that data.

The digital civil rights community has long promoted the idea that Internet based platforms and other computer systems must be designed and run in a way that is aligned with human values. In the context of AI and ML based computer systems, this has led to demands for AI fairness and transparency/explainability that have also found their way into policy like the GDPR, legislation in California, and the upcoming EU AI Act. AI fairness demands have influenced the course of AI research being done, e.g. there has been research on defining it even means for an AI model to be fair, and on making models that actually implement this meaning.

To a first approximation, privacy and digital rights advocates will care much more about what an ML model does, what effect its use has on society, than about the actual size of the ML model. So they are not natural allies for x-risk community initiatives that would seek a simple ban on models beyond a certain size. However, they would be natural allies for any initiative that seeks to design more aligned models, or to promote a growth of research funding in that direction.

To make a comment on the premise of the original post above: digital rights activists will likely tell you that, when it comes to interventions on AI research, speculating about the tractability of 'slowing down AI research' is misguided. What you really should be thinking about is changing the direction of AI research.

I think you are ignoring the connection between corporate governance and national/supra-national government policies. Typically, corporations do not implement costly self-governance and risk management mechanisms just because some risk management activists have asked them nicely. They implement them if and when some powerful state requires them to implement them, requires this as a condition for market access or for avoiding fines and jail-time.

Asking nicely may work for well-funded research labs who do not need to show any profitability, and even in that special case one can have doubts about how long their do-not-need-to-be-profitable status will last. But definitely, asking nicely will not work for your average early-stage AI startup. The current startup ecosystem encourages the creation of companies that behave irresponsibly by cutting corners. I am less confident than you are that Deepmind and OpenAI have a major lead over these and future startups, to the point where we don't even need to worry about them.

It is my assessment that, definitely in EA and x-risk circles, too few people are focussed on national government policy as a means to improve corporate governance among the less responsible corporations. In the case of EA, one might hope that recent events will trigger some kind of update.

Note: This is presumably not novel, but I think it ought to be better-known.

This indeed ought to be better-known. The real question is: why is it not better-known?

What I notice in the EA/Rationalist based alignment world is that a lot of people seem to believe in the conventional wisdom that nobody knows how to build myopic agents, nobody knows how to build corrigible agents, etc.

When you then ask people why they believe that, you usually get some answer 'because MIRI', and then when you ask further it turns out these people did not actually read MIRI's more technical papers, they just heard about them.

The conventional wisdom 'nobody knows how to build myopic agents' is not true for the class of all agents, as your post illustrates. In the real world, applied AI practitioners use actually existing AI technology to build myopic agents, and corrigible agents, all the time. There are plenty of alignment papers showing how to do these things for certain models of AGI too: in the comment thread here I recently posted a list.

I speculate that the conventional rationalist/EA wisdom of 'nobody knows how to do this' persists because of several factors. One of them is just how social media works, Eternal September, and People Do Not Read Math, but two more interesting and technical ones are the following:

  1. It is popular to build analytical models of AGI where your AGI will have an infinite time horizon by definition. Inside those models, making the AGI myopic without turning it into a non-AGI is then of course logically impossible. Analytical models built out of hard math can suffer from this built-in problem, and so can analytical models built out of common-sense verbal reasoning, In the hard math model case, people often discover an easy fix. In verbal models, this usually does not happen.

  2. You can always break an agent alignment scheme by inventing an environment for the agent that breaks the agent or the scheme. See johnswentworth's comment elsewhere in the comment section for an example of this. So it is always possible to walk away from a discussion believing that the 'real' alignment problem has not been solved.

I think I agree to most of it: I agree that some form of optimization or policy search is needed to get many things you want to use AI for. But I guess you have to read the paper to find out the exact subtle way in which the AGIs inside can be called non-consequentialist. To quote Wikipedia:

In ethical philosophy, consequentialism is a class of normative, teleological ethical theories that holds that the consequences of one's conduct are the ultimate basis for judgment about the rightness or wrongness of that conduct.

I do not talk about this in the paper, but in terms of ethical philosophy, the key bit about counterfactual planning is that it asks: judge one's conduct by its consequences in what world exactly? Mind you, the problem considered is that we have to define the most appropriate ethical value system for a robot butler, not what is most appropriate for a human.

Hi Simon! You are welcome! By the way, I very much want to encourage you to be skeptical and make up your own mind.

I am guessing that by mentioning consequentialist, you are referring to this part of Yudkowsky's list of doom:

  1. Corrigibility is anti-natural to consequentialist reasoning

I am not sure how exactly Yudkowsky is defining the terms corrigibility or consequentalist here, but I might actually be agreeing with him on the above statement, depending on definitions.

I suggest you read my paper Counterfactual Planning in AGI Systems, because it is the most accessible and general one, and because it presents AGI designs which can be interpreted as non-consequentualist.

I could see consequentialist AGI being stably corrigible if it is placed in a stable game-theoretical environment where deference to humans literally always pays as a strategy. However, many application areas for AI or potential future AGI do not offer such a stable game-theoretical environment, so I feel that this technique has very limited applicability.

If we use the 2015 MIRI paper definition of corrigibility, the alignment tax (the extra engineering and validation effort needed) for implementing corrigibility in current-generation AI systems is low to non-existent. The TL;DR here is: avoid using a bunch of RL methods that you do not want to use anyway when you want any robustness or verifiability. As for future AGI, the size of the engineering tax is open to speculation. My best guess is that future AGI will be built, if ever, by leveraging ML methods that still resemble world model creation by function approximation, as opposed to say brain uploading. Because of this, and some other reasons, I estimate a low safety engineering tax to achieve basic corrigibility.

Other parts of AGI alignment may be very expensive. e.g. the part of actually monitoring an AGI to make sure its creativity is benefiting humanity, instead of merely finding and exploiting loopholes in its reward function that will hurt somebody somewhere. To the extent that alignment cannot be cheap, more regulation will be needed to make sure that operating a massively unaligned AI will always be more expensive for a company to do than operating a mostly aligned AI. So we are looking at regulatory instruments like taxation, fines, laws that threaten jail time, and potentially measures inside the semiconductor supply chain, all depending on what type of AGI will become technically feasible, if ever.

Corrigibility with Utility Preservation is not the paper I would recommend you read first, see my comments included in the list I just posted.

To comment on your quick thoughts:

  • My later papers spell out the ML analog of the solution in `Corrigibility with' more clearly.

  • On your question of Do you have an account of why MIRI's supposed impossibility results (I think these exist?) are false?: Given how re-tellings in the blogosphere work to distort information into more extreme viewpoints, I am not surprised you believe these impossibility results of MIRI exist, but MIRI does not have any actual mathematically proven impossibility results about corrigibility. The corrigibility paper proves that one approach did not work, but does not prove anything for other approaches. What they have is that 2022 Yudkowsky is on record expressing strongly held beliefs that corrigibility is very very hard, and (if I recall correctly) even saying that nobody has made any progress on it in the last ten years. Not everybody on this site shares these beliefs. If you formalise corrigibility in a certain way, by formalising it as producing a full 100% safety, no 99.999% allowed, it is trivial to prove that a corrigible AI formalised that way can never provably exist, because the humans who will have to build, train, and prove it are fallible. Roman Yampolskiy has done some writing about this, but I do not believe that this kind or reasoning is at the core of Yudkowsky's arguments for pessimism.

  • On being misleadingly optimistic in my statement that the technical problems are mostly solved: as long as we do not have an actual AGI in real life, we can only ever speculate about how difficult it will be to make it corrigible in real life. This speculation can then lead to optimistic or pessimistic conclusions. Late-stage Yudkowsky is of course well-known for speculating that everybody who shows some optimism about alignment is wrong and even dangerous, but I stand by my optimism. Partly this is because I am optimistic about future competent regulation of AGI-level AI by humans successfully banning certain dangerous AGI architectures outright, much more optimistic than Yudkowsky is.

  • I do not think I fully support my 2019 statement anymore that 'Part of this conclusion [of Soares et al. failing to solve corrigibility] is due to the use of a Platonic agent model'. Nowadays, I would say that Soares et al did not succeed in its aim because it used a conditional probability to calculate what should have been calculated by a Pearl counterfactual. The Platonic model did not figure strongly into it.

OK, Below I will provide links to few mathematically precise papers about AGI corrigibility solutions, with some comments. I do not have enough time to write short comments, so I wrote longer ones.

This list or links below is not a complete literature overview. I did a comprehensive literature search on corrigibility back in 2019 trying to find all mathematical papers of interest, but have not done so since.

I wrote some of the papers below, and have read all the rest of them. I am not linking to any papers I heard about but did not read (yet).

Math-based work on corrigibility solutions typically starts with formalizing corrigibility, or a sub-component of corrigibility, as a mathematical property we want an agent to have. It then constructs such an agent with enough detail to show that this property is indeed correctly there, or at least there during some part of the agent lifetime, or there under some boundary assumptions.

Not all of the papers below have actual mathematical proofs in them, some of them show correctness by construction. Correctness by construction is superior to having to have proofs: if you have correctness by construction, your notation will usually be much more revealing about what is really going on than if you need proofs.

Here is the list, with the bold headings describing different approaches to corrigibility.

Indifference to being switched off, or to reward function updates

Motivated Value Selection for Artificial Agents introduces Armstrong's indifference methods for creating corrigibility. It has some proofs, but does not completely work out the math of the solution to a this-is-how-to-implement-it level.

Corrigibility tried to work out the how-to-implement-it details of the paper above but famously failed to do so, and has proofs showing that it failed to do so. This paper somehow launched the myth that corrigibility is super-hard.

AGI Agent Safety by Iteratively Improving the Utility Function does work out all the how-to-implement-it details of Armstrong's indifference methods, with proofs. It also goes into the epistemology of the connection between correctness proofs in models and safety claims for real-world implementations.

Counterfactual Planning in AGI Systems introduces a different and more easy to interpret way for constructing a a corrigible agent, and agent that happens to be equivalent to agents that can be constructed with Armstrong's indifference methods. This paper has proof-by-construction type of math.

Corrigibility with Utility Preservation has a bunch of proofs about agents capable of more self-modification than those in Counterfactual Planning. As the author, I do not recommend you read this paper first, or maybe even at all. Read Counterfactual Planning first.

Safely Interruptible Agents has yet another take on, or re-interpretation of, Armstrong's indifference methods. Its title and presentation somewhat de-emphasize the fact that it is about corrigibility, by never even discussing the construction of the interruption mechanism. The paper is also less clearly about AGI-level corrigibility.

How RL Agents Behave When Their Actions Are Modified is another contribution in this space. Again this is less clearly about AGI.

Agents that stop to ask a supervisor when unsure

A completely different approach to corrigibility, based on a somewhat different definition of what it means to be corrigible, is to construct an agent that automatically stops and asks a supervisor for instructions when it encounters a situation or decision it is unsure about. Such a design would be corrigible by construction, for certain values of corrigibility. The last two papers above can be interpreted as disclosing ML designs that also applicable in the context of this stop when unsure idea.

Asymptotically unambitious artificial general intelligence is a paper that derives some probabilistic bounds on what can go wrong regardless, bounds on the case where the stop-and-ask-the-supervisor mechanism does not trigger. This paper is more clearly about the AGI case, presenting a very general definition of ML.

Anything about model-based reinforcement learning

I have yet to write a paper that emphasizes this point, but most model-based reinforcement learning algorithms produce a corrigible agent, in the sense that they approximate the ITC counterfactual planner from the counterfactual planning paper above.

Now, consider a definition of corrigibility where incompetent agents (or less inner-aligned agents, to use a term often used here) are less corrigible because they may end up damaging themselves, their stop buttons. or their operator by being incompetent. In this case, every convergence-to-optimal-policy proof for a model-based RL algorithm can be read as a proof that its agent will be increasingly corrigible under learning.


Cooperative Inverse Reinforcement Learning and The Off-Switch Game present yet another corrigibility method with enough math to see how you might implement it. This is the method that Stuart Russell reviews in Human Compatible. CIRL has a drawback, in that the agent becomes less corrigible as it learns more, so CIRL is not generally considered to be a full AGI-level corrigibility solution, not even by the original authors of the papers. The CIRL drawback can be fixed in various ways, for example by not letting the agent learn too much. But curiously, there is very little followup work from the authors of the above papers, or from anybody else I know of, that explores this kind of thing.

Commanding the agent to be corrigible

If you have an infinitely competent superintelligence that you can give verbal commands to that it will absolutely obey, then giving it the command to turn itself into a corrigible agent will trivially produce a corrigible agent by construction.

Giving the same command to a not infinitely competent and obedient agent may give you a huge number of problems instead of course. This has sparked endless non-mathematical speculation, but in I cannot think of a mathematical paper about this that I would recommend.

AIs that are corrigible because they are not agents

Plenty of work on this. One notable analysis of extending this idea to AGI-level prediction, and considering how it might produce non-corrigibility anyway, is the work on counterfactual oracles. If you want to see a mathematically unambiguous presentation of this, with some further references, look for the section on counterfactual oracles in the Counterfactual Planning paper above.


Myopia can also be considered to be feature that creates or improves or corrigibility. Many real-world non-AGI agents and predictive systems are myopic by construction: either myopic in time, in space, or in other ways. Again, if you want to see this type of myopia by construction in a mathematically well-defined way when applied to AGI-level ML, you can look at the Counterfactual Planning paper.

Hi Akash! Thanks for the quick clarifications, these make the contest look less weird and more useful than just a 500 word essay contest.

My feedback here is that I definitely got the 500 word essay contest vibe when I read the 'how it works' list on the contest home page, and this vibe only got reinforced when I clicked on the official rules link and skimmed the document there. I recommend that you edit the 'how it works' list to on the home page, to make it it much more explicit that the essay submission is often only the first step of participating, a step that will lead to direct feedback, and to clarify that you expect that most of the prize money will go to participants who have produced significant research beyond the initial essay. If that is indeed how you want to run things.

On judging: OK I'll e-mail you.

I have to think more about your question about posting a writeup on this site about what I think are the strongest proposals for corrigibility. My earlier overview writeup that explored the different ways how people define corrigibility took me a lot of time to write, so there is an opportunity cost I am concerned about. I am more of an academic paper writing type of alignment researcher than a blogging all of my opinions on everything type of alignment researcher.

On the strongest policy proposal towards alignment and corrigibility, not technical proposal: if I limit myself to the West (I have not looked deeply into China, for example) then I consider the EU AI Act initiative by the EU to be the current strongest policy proposal around. It is not the best proposal possible, and there are a lot of concerns about it, but if I have to estimate expected positive impact among different proposals and initiatives, this is the strongest one.

Related to this, from the blog post What does Meta AI’s Diplomacy-winning Cicero Mean for AI?:

The same day that Cicero was announced, there was a friendly debate at the AACL conference on the topic "Is there more to NLP [natural language processing] than Deep Learning,” with four distinguished researchers trained some decades ago arguing the affirmative and four brilliant young researchers more recently trained arguing the negative. Cicero is perhaps a reminder that there is indeed a lot more to natural language processing than deep learning.

I am originally a CS researcher trained several decades ago, actually in the middle of an AI winter. That might explain our different viewpoints here. I also have a background in industrial research and applied AI, which has given me a lot of insight into the vast array of problems that academic research refuses to solve for you. More long-form thoughts about this are in my Demanding and Designing Aligned Cognitive Architectures.

From where I am standing, the scaling hype is wasting a lot of the minds of the younger generation, wasting their minds on the problem of improving ML benchmark scores under the unrealistic assumption that ML will have infinite clean training data. This situation does not fill me with as much existential dread as it does some other people on this forum, but anyway.

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