Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

G Gordon Worley III recently complained about a lack of precision in discussions about whether Goodhart's Law will present a fatal problem for alignment in practice. After attending a talk in which Dylan Hadfield-Menell[1] presented the "Goodhart's Law will be a big deal" perspective, I came away with a relatively concrete formulation of where I disagree. In this post I'll try to explain my model for this, expanding on my short comment here.

More specifically, in this post I'll discuss proxy misspecification, which I view as a subproblem of Goodhart's Law (but not necessarily the entirety of it).

Our model of proxy misspecification

First I'll borrow Hadfield-Menell's mathematical model of the proxy misspecification problem. Suppose:

  1. Alice has things  which she values, i.e. she has a utility function  which is increasing in each .
  2. A robot is given a proxy utility function  which depends on some strict subset of the  's.
  3. The robot optimizes  by freely varying all , subject only to resource constraints that require the  to trade off against each other.

Then, as you'd expect, it's a theorem that the robot will set to their minimum possible values all  on which the proxy utility function  does not depend (instead putting all resources to increasing the 's which  does take into account).

To borrow an example from the paper, we can imagine that the robot is a content recommendation algorithm. Then the 's might be metrics for things like ad revenue generated, engagement quality, content diversity, and overall community well-being. Humans care about all of these things, but our content recommendation algorithm might have a proxy reward that depends only on the first two. Furthermore, the algorithm only gets to recommend a limited amount of content, a resource constraint which (let's assume) prevents it from making all of these metrics as good as possible. In that case, according to the model, the algorithm will recommend content in a way that minimizes the features on which its proxy reward doesn't depend: content diversity and overall community well-being.

My complaint: it might be hard to decouple the 's

The above formulation of the proxy misspecification problem is a theorem. So if I'm going to resist the conclusion that future AI systems will destroy all value that their proxy rewards don't take into account, then I'll need to dispute part of the set-up or assumptions. Indeed, I'll attack the assumption that the robot is able to freely vary the 's subject only to resource constraints.

In reality, many of the things we care about are highly correlated over "easily attainable" world states. Or in other words, for many pairs  of things we care about, it's hard to increase  without also increasing . Furthermore, given a triple  of things we care about, it's even harder to increase  and  without also increasing . And so on for quadruples, quintuples, etc. with it getting harder to decouple one thing we value from an -tuple of other things we value as  grows larger.[2]

What work is being done by the word "hard" in the previous paragraph? One interpretation: finding ways to decouple the things human value and vary them freely requires sufficiently strong AI capabilities. And as the proxy utility function  takes into account more of the  and becomes a better approximation of , the capabilities level needed to decouple the 's on which  depends from the rest increases.

And if you buy all of this, then a corollary is: for each fixed capabilities level, there is an acceptable "margin of error" for our proxy utility functions such that if  is within this margin of error from , then an AI system with said capabilities level and proxy utility  will approximately optimize for . As capabilities improve, this safe margin of error shrinks.

Shrinking safe margin of error between proxy reward and true reward as capabilities improve.

The capabilities vs. value learning race

As time goes on, AI capabilities will improve, so the acceptable margin of error[3] between the proxy utility function  and our true utility function  will shrink. This might seem to suggest doom (since eventually AI systems will become capable enough to maximize their proxy utility functions while destroying everything else we value). 

But there is cause for hope: as general capabilities improve so might value learning capabilities. In other words, if you think that future AI systems will learn their proxy reward from humans (via human feedback, IRL, CIRL, etc.), then you can model the future as a race between increasingly accurate proxy utility functions and increasingly narrow acceptable margins for inaccuracy.

The above graphs show the two ways the race could go. On the left: value learning improves fast enough to keep proxy rewards within the margin of error; we are safe. On the right: value learning is unable to keep up; AI systems destroy much of what we value. 

Improving value learning corresponds to pushing down the yellow line. Other types of safety work, like impact regularization or work on safe exploration, might help move the green line up.[4]

It doesn't seem obvious to me how this race will go by default; in fact, the likely trajectories seem to depend on lots of empirical facts about the world that I don't have strong views on. Relevant considerations include:

  • How Goodhart-able are human values? Or more precisely, how narrow is the safe margin of error for a given capabilities level?
    • One way to get information about this is to argue a lot about air conditioner models. We can treat "the market" as an optimizer with some fixed capabilities level and a proxy utility function that takes into account things like profit generated, consumer ratings, professional reviewer ratings, etc. If optimizing for this proxy reward already produces very different outcomes than optimizing for true consumer preferences, this provides some information about the general trend for the acceptable margin of error.
  • What is the likely trajectory of value learning? Will it scale to learn proxy rewards that stay within the margin of error?
    • Will our ability to correctly evaluate how happy we are with completed tasks be a bottleneck?
    • Will our value learning algorithms have a useful inductive bias?

... as well as probably many others.

These considerations are relatively concrete and amenable to discussion. My hope is that by discussing them (and other considerations like them), the "Goodhart's Law is a fatal obstruction to alignment" people and the "Goodhart's Law won't be a big problem in practice" people won't talk past each other as much.

 

  1. ^

    This isn't the recording from the talk I attended, but it seems to be identical in content.

  2. ^

    More precisely, we shouldn't model agents as freely choosing states. Rather, we should model them as choosing actions (from a much lower-dimensional space!) which influence states. Each action might influence many of the features  that we care about, hence the apparent correlation between 's. Thanks to Ben Edelman for suggesting this model (which is present in these papers that study proxies which work well despite Goodharting).

  3. ^

    I'm going to keep using the word "error" informally to mean a measure of dissimilarity between  and , but I don't actually have in mind a particular metric that we could use to quantify how different a proxy utility function is from a given utility function. Sorry if this is confusing.

  4. ^

    Note also that depending on what you think about a whole bunch of other safety-relevant problems (mesa-optimizers, robustness to distributional shift, etc.), you might think that by default the safe margin of error for high capabilities levels is negative, i.e. that we won't be safe even with a proxy utility function that perfectly matches our true utility function. To be clear, since this is a post about proxy misspecification, I'm implicitly screening off these concerns and imagining that proxy misspecification is the only obstruction to alignment.

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Thanks for trying to make the issue more concrete and provide a way to discuss it!

One thing I want to point out is that you don't really need to put the non-constrained variables at the worst possible state; you just have the degree of freedom to put them to whatever helps you and is not too hard to reach.

Using sets, you have a set of world you want, and a proxy that is a superset of this (because you're not able to aim exactly at what you want). The problem is that the AI is optimizing to get in the superset with high guarantees and stay there, and so it's probably aiming for the easiest part of the set to reach and stay in (submitted to the accessibility constraints that you mention). This is what should lead to instrumental convergence and the real issue with the proxies IMO.

It doesn't seem obvious to me how this race will go by default; in fact, the likely trajectories seem to depend on lots of empirical facts about the world that I don't have strong views on.

Let me propose another framing: there are less possible worlds in which the curves are "nice". The good case is more specific, more constrained, and thus there are more ways things can go wrong. This doesn't mean things will definitely go wrong or that there's no argument that could convince us that the situation will be good by default. Just that the burden of proof is on showing that the good but less numerous worlds are somehow privileged by Reality.

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