Safety isn’t safety without a social model (or: dispelling the myth of per se technical safety)

Strongly agree.

Related: It's disheartening to recognize, but it seems the ML community might not even get past the first crucial step in reducing risks, which is understanding them. We appear to live in a world where most people, including key decision-makers, still don't grasp the gravity of the situation. For instance, in France, we still hear influential figures like Arthur Mensch, CEO of Mistral, saying things like, "When you write this kind of software, you always control what's going to happen, all the outputs the software can have." As long as such individuals are leading AGI labs, the situation will remain quite dire.

+1 for the conflationary alliances [? · GW] point. It is especially frustrating when I hear junior people interchange "AI Safety" and "AI Alignment." These are two completely different concepts, and one can exist without the other. (The fact that the main forum for AI Safety is the "Alignment Forum" does not help with this confusion). I'm not convinced the goal of the AI Safety community should be to align AIs at this point.

However, I want to make a small amendment to Myth 1: I believe that technical work which enhances safety culture is generally very positive. Examples of such work include scary demos like "BadLlama," which I cite at least once a week, or benchmarks such as Evaluating Frontier Models for Dangerous Capabilities, which tries to monitor particularly concerning capabilities. More "technical" works like these seem overwhelmingly positive, and I think that we need more competent people doing this.

Yeah, this agrees with my thinking so far. However, I think if you could research how to align AIs specifically to human flourishing (as opposed to things like obedience/interpretability/truthfulness, which defer to the user's values), that kind of work could be more helpful than most.

Good article. There especially needs to be more focus on the social model. I am an optimist, however I think ~half of how things could go wrong with AI is with alignment technically solved. The more you expect a slow takeoff or non-fatal overhang, the more this is important.

I don't find this framing compelling. Particularly wrt to this part:

Obedience — AI that obeys the intention of a human user can be asked to help build unsafe AGI, such as by serving as a coding assistant. (Note: this used to be considered extremely sci-fi, and now it's standard practice.)

I grant the point that an AI that does what the user wants can still be dangerous (in fact it could outright destroy the world). But I'd describe that situation as "we successfully aligned AI and things went wrong anyway" rather than "we failed to align AI". I grant that this isn't obvious; it depends on how exactly AI alignment is defined. But the post frames its conclusions as definitive rather than definition-dependent, which I don't think is correct.

Is the-definition-of-alignment-which-makes-alignment-in-isolation-a-coherent-concept obviously not useful? Again, I don't think so. If you believe that "AI destroying the world because it's very hard to specify a utility function that doesn't destroy the world" is a much larger problem than "AI destroying the world because it obeys the wrong group of people", then alignement (and obedience in particular) is a concept useful in isolation. In particular, it's... well, it's not definitely helpful, so your introductory sentence remains literally true, but it's very likely helpful. The important thing is does make sense to work on obedience without worrying about how it's going to be applied because increasing obedience is helpful in expectation. It could remain helpful in expectation even if it accelerates timelines. And note that this remains true even if you do define Alignment in a more ambitious way.

I'm aware that you don't have such a view [LW · GW], but again, that's my point; I think this post is articulating the consequences of a particular set of beliefs about AI, rather than pointing out a logical error that other people make, which is what its framing suggests.

As far as I know, the answer is simply that you have to model the social landscape around you and how your research contributions are going to be applied.

In other words, it matters who receives your ideas, and what they choose to do with those ideas, even when your ideas are technical advances in AI safety or "alignment". 

Like others I agree. 

Some of what you're saying I interpret as an argument for increased transdisciplinarity in AI (safety) research. It's happening, but we would all likely benefit from more. By transdisciplinarity I mean collaborative work that transcends disciplines and includes non-academic stakeholders. 

My technical background is climate science not AI, but having watched that closely, I’d argue it’s a good example of an (x/s)risk-related discipline that (relative to AI) is much further along in the process of branching out to include more knowledge systems (transdisciplinary research). 

Perhaps this analogy is too locally specific to Australia to properly land with folks, but AI-safety does not have the climate-safety equivalent of "cultural burning" and probably won't for some time. But it will eventually (have to – for reasons you allude to). 

Some of what you’re saying, especially the above quoted, I think relates deeply to research ethics. Building good models of the social landscapes potentially impacted by research is something explored at depth across many disciplines. If I can speak frankly, the bleeding edge of that research is far removed from Silicon Valley and its way of thinking and acting.

Yes, agreed. And: the inevitable dual-use nature of alignment research shouldn't be used as a reason or excuse to not do alignment research. Remaining pure by not contributing to AGI is going to be cold comfort if it arrives without good alignment plans.

So there is a huge challenge: we need to do alignment research that contributes more to alignment than to capabilities, within the current social arrangement. It's very tough to guess which this is. Optimistic individuals will be biased to do too much; cautious individuals will tend to do too little.

One possible direction is to put more effort toward AGI alignment strategy. My perception is that we have far less than would be optimal, and that this is the standard nature of science. People are more apt to work on object level projects rather than strategize about which will best advance the field's goals. We have much more than average in AGI safety, but the large disagreements among well-informed and conscientious thinkers indicates that we could still use more. And it seems like such strategic thinking is necessary to decide which object-level work is will probably advance alignment faster than capabilities.

Physics Myths vs reality.

Myth: Ball bearings are perfect spheres. 

Reality: The ball bearings have slight lumps and imperfections due to manufacturing processes.

Myth: Gravity pulls things straight down at 9.8 m/s/s.

Reality: Gravitational force varies depending on local geology.

You can do this for any topic. Everything is approximations. The only question is if they are good approximations.

I'm not certain that Myth #1 is a necessarily myth for all approaches to AI Safety. Specifically, if the Value Learning [? · GW] approach to AI safety turned out to be the most effective one, then the AI will be acting as an alignment researcher and doing research (in the social sciences) to converge its views on human values to the truth, and then using that as an alignment target. If in addition to that, you also believe that human values are a matter of objective fact (e.g. that if they are mostly determined by a set of evolved Evolutionary Psychology adaptations to the environmental niche that humans evolved in), and are independent of background/cilture/upbringing, then the target that this process converges to might be nearly independent of the human social context in which this work started, and of the desires/views/interests of the specific humans involved at the beginning of the process.

However, that is a rather strong and specific set of assumptions required for Myth #1 not to be a myth: I certainly agree that in general and by default, for most ideas in Alignment, human context matters, and that the long-term outcome of a specific Alignment technique being applied in, say, North Korea, might differ significantly from it being applied in North America.

AI that obeys the intention of a human user can be asked to help build unsafe AGI, such as by serving as a coding assistant.

I think a better example of your point is "Corrigible AI can be used by a dictator to enforce their rule."

Minor: these days "compute overhang" mostly means something else [LW · GW].

Designing new technology seems to be what I'm best at, but I've often asked myself:

What's the point of new technology if all it does is give institutions more room to be corrupt?

Computers got much faster, but software and web pages bloat until they're slow, until there's enough of a problem that management gets pushback and has to make changes they otherwise wouldn't. More technology can improve the results of a small amount of effort, but any amount of technology multiplied by zero effort from leadership towards a goal is zero.

One additional maxim to consider is that the AI community in general can only barely conceptualize and operationalize difficult concepts, such as safety. Historically, the AI community was good at maximizing some measure of performance, usually pretty straight forward test set metrics such as classification accuracy. Culturally this is how the community approaches all the problems -- by aggregating complex phenomena into a single number. Note that this approach is not used in that many fields outside of AI and math, as you always have to make some lossy simplifications.

We can observe this malpractice in AI safety as well. There is a cottage industry of datasets and papers collecting "safety" samples, and we  use these to measure some safety metric. We can then compare the numbers for different models and this makes AI folks happy. But there is barely any discussion about how representative these datasets really are for real-life risks, how comprehensive the data collection process is, or how sound it is to use random crowd-sourced workers or LLMs to generate such samples. The threats and risks are also rarely described in more detail -- often it's just a lot of hand-waving.

Based on my pretty deep experience with one aspect of AI safety (societal biases), I have very little confidence in our ability to understand AI behavior. Compared to measuring performance on well-defined NLP tasks, once we involve societal context, the intricacies of what we are trying to measure are beyond simple benchmarks. Note that we have entire fields that are trying to understand some of these problem in human societies, but we are to believe that we can collect a test set with few thousand samples and this should be enough to understand how AI works.

Curated.

The overall point here seems true and important to me.

I think I either disagree, or am agnostic about, some of the specific examples given in the Myth vs Reality section. I don't think they're loadbearing for the overall point. I may try to write those up in more detail later.

Technical AI safety and/or alignment advances are intrinsically safe and helpful to humanity, irrespective of the state of humanity.

I think this statement is weakly true, insofar as almost no misuse by humans could possibly be worse than what a completely out of control ASI would do. Technical safety is a necessary but not sufficient condition to beneficial AI. That said, it's also absolutely true that it's not nearly enough. Most scenarios with controllable AI still end with humanity nearly extinct IMO, with only a few people lording their AI over everyone else. Preventing that is not a merely technical challenge. 

I would have liked to write a post that offers one weird trick to avoid being confused by which areas of AI are more or less safe to advance, but I can’t write that post. As far as I know, the answer is simply that you have to model the social landscape around you and how your research contributions are going to be applied.

Another thing that can't be ignored is the threat of Social Balkanization, Divide-and-conquer tactics have been prevalent among military strategists for millennia, and the tactic remains prevalent and psychologically available [? · GW] among the people making up corporate factions and many subcultures (likely including leftist and right-wing subcultures).

It is easy for external forces to notice opportunities to Balkanize a group, to make it weaker and easier to acquire or capture the splinters, which in turn provides further opportunity for lateral movement and spotting more exploits. Since awareness and exploitation of this vulnerability is prevalent, social systems without this specific hardening are very brittle and have dismal prospects.

Sadly, Balkanization can also emerge naturally, as you helpfully pointed out in Consciousness as a Conflationary Alliance Term [LW · GW], so the high base rates make it harder to correctly distinguishing attacks from accidents. Inadequately calibrated autoimmune responses are not only damaging, but should be assumed to be automatically anticipated and misdirected by default, including as part of the mundane social dynamics of a group with no external attackers.

There is no way around the loss function.

Wow, such a badly argued( aka BS) while heavily up-voted article!

Let's start with the Myth #1, what a straw-man! Rather than this extreme statement, most researchers likely believe that in the current environment their safety&alignment advances are likely( with high EV) helpful to humanity. The thing here is they had quite a free hand or at least varied options to pick the environment where they work and publish.

With your examples a bad actor could see a worthy EV even with a capable system that is less obedient and more false. Even if interpretabilty speeds up development, it would direct such development to more transparent models, at least there is a naive chance for that.

Myth #2: I've not yet met anybody in the alighnment circles who believed that. Most are pretty conscious about the double-edgedness and your sub-arguments.

https://www.lesswrong.com/posts/F2voF4pr3BfejJawL/safety-isn-t-safety-without-a-social-model-or-dispelling-the?commentId=5vB5tDpFiQDG4pqqz [LW(p) · GW(p)] depicts the flaws I point to neatly/gently.

    But with ambitious spears they made us change
    They crouched behind their mirrors and fought on.

    Robin Williamson, Creation

I go back to "know thyself" as a solution, but I think it needs to happen at species scale due to the times, so it needs to effectively be a lot more addictive than heretofore. ML is perfect for this, because one uses one's own data, so no artists or even corporations are being ripped off. If the buttons that others are pushing can be understood, all kinds of manipulation might be stymied. 

Such an app would need to be a tetris-like way to understand emotions, to golf cart around Plato's cave before going deeper with a flashlight to see what tunnels time has made, giving a feeling of being understood as an incentive. I've been working on one of the two apps that popped into my head for that (Phobrain), and just open sourced a workbench for building a representation of self by labeling 'Rorschach pairs' of pics yes/no. The other app would be worry beads with axial sensing and force feedback, with vibration and temperature also possible. Aside from exploring oneself and holding the hand of god, one might hold hands with someone around the world or across the table in a meeting, or carry the beads in one's underwear - there should be enough today's-world possibilities there for moneymaking products that could be adapted for next-world understanding.

This feels like I'm not addressing the formal risk aspect for the industry, but that angle of discussion seems unlikely to withstand the changes that the first 'killer app' that decides a battle or war will bring. The momentum of evolution with war will not be stopped by people discussing it, only by choosing a new model at a species scale.

Broadly agree, in that most safety research expands control over systems and our understanding of them, which can be abused by a bad actor.

This problem is encountered by for-profit companies, where profit is on the lines instead of catastrophe. They too have R&D departments and research directions which have the potential for misuse. However, this research is done inside a social environment (the company) where it is only explicitly used to make money.

To give a more concrete example, improving self-driving capabilities also allows the companies making the cars to intentionally make them run people down if they so wish. The more advanced the capabilities, the more precise they can be in deploying their pedestrian-killing machines onto the roads. However, we would never expect this to happen as this would clearly demolish the profitability of a company and result in the cessation of these activities.

AI safety research is not done in this kind of environment at present whatsoever. However, it does seem to me that these kinds of institutions that carefully vet research and products, only releasing them when they remain beneficial, are possible. 

The LessWrong Review [? · GW] runs every year to select the posts that have most stood the test of time. This post is not yet eligible for review, but will be at the end of 2025. The top fifty or so posts are featured prominently on the site throughout the year.

Hopefully, the review is better than karma at judging enduring value. If we have accurate prediction markets on the review results, maybe we can have better incentives on LessWrong today. Will this post make the top fifty?

Thank you for pointing this out, as it is very important. 

The morality / ethics of the human beings matters a lot. But it seems to matter more than just a lot. If we get even a little thing wrong here, ...

But we're getting more than just a little wrong here, imo.  Afaict most modern humans are terribly confused about morality / ethics. As you say "what is even good"

I've spoken with serious mathematicians who believe they might have a promising direction to the AI alignment problem. But they're also confused about what's good. That is not their realm of expertise. And math is not constrained by ethics; you can express a lot of things in math, wholesome and unwholesome. And the same is so with the social dynamics, as you point out. 

This is why MAPLE exists, to help answer the question of what is good, and help people describe that in math. 

But in order to answer the question for REAL, we can't merely develop better social models. Because again, your point applies to THAT as well. Developing better social models does not guarantee 'better for humanity/the planet'. That is itself a technology, and it can be used either way. 

We start by answering "what is good" directly and "how to act in accord with what is good" directly. We find the true 'constraints' on our behaviors, physical and mental. The entanglement between reality/truth and ethics/goodness is a real thing, but 'intelligence' on its own has never realized it.