Why Not Just Outsource Alignment Research To An AI?

It sounds like your claim is that having the talent to potentially execute nonstandard tasks is a necessary, though not always sufficient, criteria to identify the same talent in others. Therefore, only an omni-talented executive is capable of successfully leading or outsourcing the project. They might not immediately be able to execute the nitty-gritty details of each task, but they would be capable of rapidly skilling up to execute any such task if required.

I am curious to know what you think of the following idea about how to get around this bottleneck of for omni-talented leadership, at least in certain cases.

In many cases, there is a disconnect between the difficulty of engineering and the difficulty of evaluating the product.

The iPhone was hard to engineer, but it was easy to see it made calls, played music, browsed the internet, and was simple to use.

Apollo 11 was hard to engineer, but it was easy to see the astronauts landing on the moon and returning to Earth.

The nuclear bomb was hard to engineer, but it was easy to see Fat Man and Little Boy had destroyed Hiroshima and Nagasaki.

The Tesla was hard to engineer, but it was easy to see that it required no gasoline and achieved the promised driving range.

The mRNA COVID-19 vaccine was hard to engineer, but it was easy to run a conventional vaccine trial to show that it worked.

ChatGPT was hard to engineer, but it is easy to see that it can produce nearly human-like text outputs in response to open-ended prompts.

In any of these cases, a well-funded non-expert businessperson could have placed a bounty to motivate experts to build them the desired product. For example, John F. Kennedy could have promised $500 million to any American organization that could prove they had successfully landed American astronauts on the moon.

Of course, building the rocket and the mission logistics might have required omni-talented leadership in rocket design and space mission logistics. But the essential point is that it would not have required Kennedy to personally have that level of capability in order to achieve his desired result.

This generalizes. Omni-talented leadership is not required for outsourcing when a project satisfies three conditions:

1. Verifiable: The overall success of the project is easy to evaluate by a non-expert.
2. Bounty-compatible: It's possible to broadcast an offer of a credible and sufficiently motivating reward in exchange for a product that achieves that result.
3. Achievable: A group of omni-talented experts can put together the resources, including human resources, to pursue the bountys successfully.

This is a powerful result. It means that any achievable task can be outsourced by a non-expert, as long as the non-expert can decompose it into verifiable and bounty-compatible parts.

That means Steve Jobs did not need the ability to design each component of the iPhone. He needed the ability to judge whether or not the product concept of a phone + iPod + computer made business sense, and what made prototype designs easy or difficult to use. He also needed the ability to motivate engineers to achieve these results with money, status, and power within Apple.

This also helps explain why it was not possible for Louis XV to motivate production of a smallpox vaccine [LW · GW]. Although a vaccine was achievable, and Louis XV had a lot of money and status to offer, there was no standard way to evaluate medical efficacy at the time, as you point out in the essay.

In the comedy sketch you linked to, the fundamental problem is that the clients don't know how to express what they really want, and the expert is failing to recognize that fact. Instead of finding an empathic way to clarify their true goal, he is criticizing their word choice. Simultaneously, the clients and the expert's boss are threatening his status rather than attempting to proactively address the communication gap. This is a situation where the project is verifiable (the clients will probably know what they want when they see it), and it may be achievable, but it is not bounty-compatible because the client doesn't know how to broadcast their desired result effectively and open conversation is inhibited by status threats and signaling games.

Under this conception, if AI alignment research can't be outsourced to an AI, then it also can't be achieved by humans. Let's assume technical AI alignment is achievable, and accept the many claims that abundant funding and rewards exist for anyone who does achieve technical AI alignment. Then all we need is a legible way to verify that technical AI alignment has, in fact, been achieved, in order to outsource it.

If we do have a reliable method of verification, then it doesn't matter whether we outsource to AI or to humans, except insofar as there's a different in our ability to motivate each.

If we do not have a reliable method of verification, then it still doesn't matter whether we outsource or not, because we will not be able to trust any proposed solution to technical AI alignment from AI or from humans.

I fully understand and agree that verifiability of a solution is the hard part, doesn't exist at present, and is the reason why, at present, we cannot "just outsource alignment research to an AI." My point is that it is possible for non-omni-talented leaders to outsource nonstandard projects and achieve optimal results, so long as project success is verifiable, achievable, and bounty-compatible. I think this is the normal way projects containing nonstandard elements get built in the real world. Fundamentally, however, I think we are on the same page about the difficulty of outsourcing AI alignment.

But you don't need to be able to code to recognize that a software is slow and buggy!?

About the terrible UI part I agree a bit more, but even there one can think of relatively objective measures to check usability without being able to speak python.

It seems like the fundamental cause of the problem with your uncle's customer tracking app is some combination of:

1. He paid for ongoing effort, rather than delivering satisfactory results. Instead of a bounty model, he used a salary or wage model to pay the programmer.
2. He lacked the ability to describe what exactly would make the app satisfactory, impairing his ability to pay for results rather than effort.

In other words, the "bounty-compatible" criteria for outsourceability was not met in this case. This raises the question of what to do about it.

If he didn't know how to specify all his performance requirements, could he have hired somebody to help him do so?

If he'd tried to outsource identifying performance requirements, could he have applied the bounty model to that job?

If he had offered a bounty in exchange for an app meeting his requirements, would his offer of a bounty have been believable?

If his offer of a bounty was believable, would a competent programmer have been willing to pursue that bounty?

As we pose these questions, we see that society's overall ability to outsource effectively is bottlenecked by the availability of high-quality bounty offer interfaces [LW · GW]. A bounty offer interface should help the user define a satisfactory result, broadcast bounty offers to a competent professional network, and make the bounty offer credible.

it sounds like there have been some attempts at creating bounty interfaces for app development. One active site for this purpose is replit. However, as I scan some of their open bounties, the problem description, acceptance criteria, and technical details seem woefully underspecified, with no apparent ability to make bounty offers credible, and I also don't see any signs that replit is plugged into a competent developer network.  Bepro is another such site, but has a confusing interface and the same problems as replit. If I was an employer or programmer, I would probably not waste my time on either of these websites. Some major companies, like Intel, have bug discovery bounty programs.

Overall, it seems like it's more difficult to build a market for bounty-based contracts. With a wage- or salary-based system, a worker can minimize their losses by quitting if their employer stops paying them. The employer doesn't need to have the requirements completely specified up front in order to attract talent. Trust can be built on the basis of reputation, willingness to pay, and cultivation of a relationship. Financial rewards for doing work are immediate, and employers get to see the product being built and adjust it as their requirements change in a dynamic business environment. On top of that, wage- and salary-based models are familiar, so it's easy to attract participants. There is some ability for a non-expert to identify good freelance wage/salary-based coders by selecting highly-paid coders with excellent reviews, which in turn incentivizes those coders to earn good reviews by producing quality software in a reasonable timeframe and budget. 

For all of these reasons, in practice, bounties may not be a realistic way to pay for nonstandard goods and services in many cases, sharply limiting the ability to outsource without an omni-competent leader to organize the effort. But perhaps there is an opportunity for someone to deal with the failure modes of bounty-based models and create a new and improved marketplace for bounty-based app development?

Fair. I am fairly confident that (1) the video at the start of the post is pointing to a real and ubiquitous phenomenon, and (2) attempts to outsource alignment research to AI look like an extremely central example of a situation where that phenomenon will occur. I'm less confident that my models here properly frame/capture the gears of the phenomenon.

Here is my attempt at a shorter answer (although it didn’t end up as short as planned) 🙂

I’m also being more simplistic here (at times deliberately so), in the hope of making “core” concepts digest with less effort.

If you don’t respond here you probably won’t hear from me in a while.

It can, sure, but how can a human get it to state those regularities (...)?

Score-functions would score argument-step-networks. It is these score-functions that would leverage regularities for when human evaluations are “good”/correct.

Here are some things that might be the case for any given score-function:

• It has Low Wiggle-room (LW): It has low wiggle-room (that is to say, it’s not possible to construct high-scoring argument-step-networks that argue in favor of contradictory conclusions)
• It Robustly Leverages Regularities for “good” human evaluations (RLR): It robustly separates out “good” human evaluations (without false positives).
• It Contains Intelligence (CI): It is “intelligent”, and reasons itself towards favoring specific conclusions.
• It Hardcodes Bias (HB): It has "hardcoded" bias in favor of specific conclusions.

Here are some things I assume regarding these properties:

1. We want RLR.
2. RLR implies LW, but LW does not imply RLR.
3. LW implies RLR and/or CI and/or HB.
4. We want to make it so that LW implies RLR (by eliminating other plausibilities).
5. We can determine if LW is the case if our capabilities at gradient descent are sufficiently good (and we know that they are).

If we are sufficiently good at gradient descent (and know that we are), we can figure out if LW is the case.

Our job would be to make it so that the most high-scoring score-functions (that it’s possible for AIs to make) would achieve LW by RLR. We could make P(RLR | LW) high by doing as follows when adding restrictions and optimization-criteria for score-functions:

1. We can give restrictions (and give bonus/penalty-points) based on source code length, processing power, etc.
2. We can make it possible to reference predictions of human answers to questions.
   1. These answer-predictions can be referenced as system-calls. The internals of functions that make answer-predictions don’t count as part of the source code length (or processing power, etc)!
   2. We can restrict the questions that are allowed (to ones that seem likely to help with RLR, and unlikely to help with HB)
   3. We can restrict the amounts of “question-templates” that are allowed (so that no more are allowed than needed to achieve RLR)
3. We can make it possible to reference heuristics that we suspect help with RLR
   1. For example average sentence length or total text length (those are simple examples - we could imagine more sophisticated ones also)
   2. Such heuristics would be calculated in external functions that are referenced as system-calls. The internals of these external functions would not count as part of the source code length (or processing power, etc).
4. Humans could specify “code-snippets”, which are kind of like regular expressions. Humans will often have an inkling for how specific parts of the score-function could work to achieve RLR (even if we haven’t worked out all the details).
   1. For various parts of the score-function, using some specific “code-snippet” may be obligatory. Or maybe the score-function would have to be made out of nothing else than “code-snippets”.
   2. If “code-snippets” are not obligatory for all parts of the score-function, source code that isn’t from a snippet could be penalized more (in terms of source code length penalty).
5. “Code-snippets” may also help enforce modularity, and enforce “abstraction” of data.
   1. For any part of the score-function it should be as hard as possible to adapt it to what the argument-step-network argues for or against.
   2. AIs should not be able to reliably guess what the argument-step-network argues for or against from looking at any input that is received by any sub-routine.
6. #1 will (if done to a sufficient extent) make CI totally infeasible (and #4 helps make that even more assured).
7. #2 and #3 makes it so that achieving LW with RLR is possible (despite of #1). But it does not make it possible to achieve LW with HB/CI.
8. #2 and #3 make RLR easier (in terms of passing requirements and maximizing score-function score). If done well they don’t make HB easier (at least not to a comparable degree).
9. #1 and #2 makes it so that achieving LW with RLR results in a higher score compared to achieving LW with RLR + a bit of HB. (Why? Because RLR is more general, and hence reduces more wiggle-room per extra complexity that is added to the score-function. Hence minimizing HB, to 0 if possible, results in less score penalty.)
10. #4 and #5 make HB harder.

And low wiggle-room has to be kept low while reaching correct conclusions. These conclusions can relate to:

• Things we know
• Predictions relating to experiments that we can carry out safely+easily (or existing experimental data)
• The behavior (in terms of e.g. output) of any function that is made by the AI or made step by step from within the argument-step-network

In other words: The score-function has to be “watertight” in relation to an incomprehensibly large scope of possible conclusions/predictions. Many of these would be no less challenging than the strawberry copy challenge, and would be in need of similar capabilities to be developed/verified from within the argument-step-network!

If a score-function is not “watertight”, it becomes easy for capable AGIs to generate conclusions that are contradictions or that easily/safely can be shown to be wrong.

Ask yourself, which is harder?:

• Making a bowl that leaks water in certain very specific conditions - e.g. on Sunday afternoons (but otherwise tends to be very watertight).
• A bowl like the one described above, except that it doesn’t leak in those very specific conditions.

Between A and B, A is often easier:

1. Making something be very reliable in general
2. Making something be very reliable in general, but have certain specific and idiosyncratic exceptions from this (where instead of working reliably it gets things reliably wrong)

In order for score-functions for argument-step-networks to lead us astray, it isn’t enough for B to not be harder than A. B would have to be easier than A!

If A or B are of equal difficulty, it would be possible to make high-scoring score-functions that give high scores to contradictory argument-step-networks. If a human-defined score-function score-function allows for contradictory conclusions, then score-functions that get a high score by this score-function score-function cannot be trusted (even if internally they have low wiggle-room)!

It can, sure, but how can a human (...) tell that it has stated them accurately?

We can define “spaces” of possible score-function score-functions (and score-function restrictions). In this space we have:

• Lots of different ways to be “strict” (that we think make HB and CI harder)
• Various optimization-criteria based on which we can redact/add points (where more points corresponds with optimizing for criteria that we think make HB/CI comparatively harder compared to RLR)

For any given restriction/optimization-criteria we may have various degrees of confidence for how this affects the ease of RLR vs HB/CI (for AIs that try to maximize score).

If we are right about our assumptions we will see clear patterns emerge:

• Any high-scoring score-function that only allows for correct predictions/conclusions in regards to things we can test safely/easily, are also internally consistent (amongst each other) in regards to predictions/conclusions we can’t test safely/easily.
• All/most high-scoring score-functions converge on the same set of possible conclusions/predictions (that it’s possible to argue in favor of with high-scoring argument-step-networks)
• The “stricter” the score-functions are, the more they converge (towards the same set of possible conclusions/predictions).
• The more confident we feel that any given restriction/optimization-target benefits P(RLR | LW), the more adding these restrictions/optimization-target makes score-functions converge (towards the same set of possible conclusions/predictions).

Meanwhile, if we are not right about assumptions, then we would see that for various claims it’s possible to argue for both “X” and “not X”, with argument-step-networks that are given a high score by ≥1 high-scoring score-function. We would not see high-scoring score-functions converge towards specific conclusions/predictions. (In which case we hopefully didn’t put all of our eggs into this specific alignment strategy 🙃)

More clear from this description what I have in mind?

Not rewarding contradictory conclusions is not a sufficient condition for a score-function to reward truth, or not reward falsehood.

Indeed!

It's a necessary but not sufficient condition.

It can, sure, but how can a human get it to state those regularities (...)?

Summary:

The regularities are expressed in terms of score-functions (that score argument-step-networks)^[1]. We can score these score-functions based on simplicity/brevity, and restrict what they can do (make it so that they have to be written within human-defined confines).

I posit that we probably can do this in such a way that the most high-scoring score-functions that it’s possible to make (that meet requirements relating to wiggle-room and “reach”^[2]) do so by specifying general regularities for when humans are/aren’t led astray (as opposed to ones that favor specific conclusions).

It should (I posit) be feasible to make it so that score-functions that are designed with the intention of fooling us about specific things earn significantly less points (if they pass the requirements at all).

If we allow score-functions to be constructed freely, then this makes it easy for AIs to output score-functions that (practically speaking) allow for pretty much any internally consistent set of conclusions. Indeed, the AI could simply have the score-function be itself, and choose to only give a high score to argument-step-trees with conclusions it likes.

So the question becomes: How do we restrict the “space” of possible score-functions that can be suggested, and add requirements that the score-functions must pass, so that the only way to get a high score (for AIs that suggest score-functions) is to make score-functions that (in some sense) separate out “good” human evaluations?

Here are examples of some of the types of possible requirements for score-functions:

The list above is not at all intended to be comprehensive. Hopefully it gives an impression of the kinds of restrictions I have in mind.

It can, sure, but how can a human (...) tell that it has stated them accurately?

1: Wiggle-room of score-functions can be explored^[11]. If a score-function is revealed to have wiggle-room, then the regularities weren’t stated “accurately”^[12].

How strong that indication is depends on our initial assumptions regarding P(high-scoring arguments can be trusted | score-function has low wiggle-room).

2: This leaves several questions, one of which is: If we specified requirements/desiderata that seemed to us that they should result in a very high P(high-scoring arguments can be trusted | score-function has low wiggle-room), how do we check if we were right? 

Answer: The job of AIs that propose score-functions is to propose score-functions that pass our requirements and get a high score. They may be tasked to find examples of score-functions (that both pass the requirements + get a high score) that are contradictory in terms of the conclusions that it’s possible to argue for with high-scoring argument-networks.

What we do here is, in a sense, to explore wiggle-room “one level higher up”. 

Suppose that A is some set/space of possible score-functions, and we think that for all of the score-functions in A, P(high-scoring arguments can be trusted | score-function has low wiggle-room) is very high. If so, then for any claim P1 (that we are interested in) we could task AIs to:

• Construct score-function SF1 (SF1 must be in A + have low wiggle-room)
• Construct score-function SF2 (SF2 must also be within A + have low wiggle-room)
• Construct an argument-step-network that concludes P1, and is assigned a high score by SF1
• Construct an argument-step-network that concludes ¬P1, and is assigned a high score by SF2

If any AI is able to do this, then this indicates that our assumptions were wrong^[13].

If we presume that some restriction/restrictions makes it disproportionately harder for score-functions to have low wiggle-room for the “wrong” reasons, then that can (sort of) be tested. Or at least we may be able to get very strong indications for whether this is the case.

This process of exploring wiggle-room “one level higher up” can be taken further [LW(p) · GW(p)]. Maybe that intuitively seems like "bells and whistles". But to me it seems like an important part of the answer. And I'm explaining it less well than I'd want to in order to make things shorter and less convoluted^[14].

3: AIs could be tasked to try making high-scoring argument-step-networks with conclusions/predictions that can be proven wrong in a safe/cheap way.

For example: Constructing functions from within the argument-network, and making wrong conclusions/predictions relating to output from those functions.

If the score-function is not "watertight", this kind of thing would offer lots of opportunitities to achieve unambiguous contradictions.

Keep in mind how “interconnected” truth-claims are. And keep in mind that predictions of human evaluations would relate to individual argument-steps (that are evaluated in a fairly “modular” way, independently of the network as a whole, and often/typically with no way to guess what the conclusion of the network is, or what questions the conclusion answers).

Imagine "proof-like" reasoning relating to the behavior of code, where each argument-step relating to the code is focused on one little thing. The kinds of regularities needed to ensure that the scoring of such argumentation is correct - well, it would need to be quite "watertight" (in order to not be exposed by AIs that are "incentivized" to do so).

And while being so "watertight" it needs (1) allow for certain fairly specific errors and (2) prevent certain specific correct conclusions that correspond inversely to those errors.

Kind of like making a bowl be very watertight in general, but making the bowl always leak water in certain very specific conditions (even those conditions cannot be determined locally by the different parts of the bowl that is to allow the water through). And having the design of this bowl be simpler (not just the same level of complexity) as the simplest way to make a bowl that doesn't leak water in these highly specific conditions (but otherwise behaves the same)^[15].

The analogy above is maybe not perfect, but not so bad either IMO (if we accept certain presumptions, e.g. that the AIs that explore wiggle-room do so competently and without bias!).

We can also test how good score-functions are in terms of e.g. predictions of experimental results (where the predictions are the conclusions of high-scoring networks). Not all experiments are safe to test, but we can ask questions relating to ones that are (and ask in regards to already-existing experimental data that the AIs don't have access to).

The AIs that make argument-step-networks would be scored based on their ability to make high-scoring argument-step-networks (and would not be penalized if it turns out that the conclusions are wrong in ways that we quickly/safely can check). AIs that make score-functions would be penalized for this if/when accuracy against such predictions has been made to be one of the score-function requirements (for the request in question).

To put it somewhat simplistically (things absorb less easily with more caveats and nuance):

In order to fool us about specific things, a score-function would need to not make it infeasible to lead us astray about anything^[16] except some very specific things (that all are outside of the realm of things we can test safely/cheaply/quickly). It has to do that within the score-function restrictions that are designed with the purpose of making that hard.

And even though it is presumably harder to fail in these very specific ways (while getting things right most of the time), it has to be (1) feasible to do that and (2) infeasible to not do that.

That is to say: The human-defined score-function-restrictions^[17] have to somehow make it infeasible to lead us astray in regards to most conclusions, but at the same time make it infeasible to not lead us astray in regards to certain specific conclusions. If not, the score-function-restrictions would have wiggle-room, and various AIs would be leaving points on the table by not pointing this out^[18].

So the human-defined restrictions mustn’t just not work. All/most of them must fail in the same very specific and improbable way.

And if we have humans define things at a "higher level" (which I'd recommend), then this mustn't just be the case for a specific set of restrictions we have defined for score-functions (but very strong patterns observed within human-defined "spaces" of possible ways that score-function restrictions can be combined).

Some postulations/assumptions that I don’t discuss here, but could get into if requested (I’m pretty much a jukebox when it comes to this):

• Whether it is realistic to obtain systems that earnestly try to explore wiggle-room (and never pass on an opportunity to expose wiggle-room when requested to look for examples of wiggle-room). And ideas/principles for how we might safely obtain/verify such systems.
• Whether it is realistic to obtain systems that predict human answers/evaluations (with the necessary scope, accuracy, precision, and non-bias). And ideas/principles for how we might safely obtain/verify such systems.
• (there are other ones also, but these ones stand out to me)

One dilemma:

We want to test/verify the accuracy of systems that predict human evaluations / answers to questions. But often we want these systems to predict human evaluations of AGI-generated content. So if we want to test the accuracy of these systems against the full range of outputs that we use, that would (seemingly) require us to expose real humans to AGI-generated content.

I would say that maybe around half of my thinking, if not more, relates to this kind of dilemma ("How might we evaluate the accuracy of functions outside of the range of inputs where we are willing+able to test output, without having humans try to understand the source code, or even look at it?").

I have also thought considerably about how we might obtain functions that handle certain types of requests, without ever intentionally leaving points on the table for specific requests (e.g. “deliberately” failing to notice specific cases where wiggle room can be exposed).

The situation I often imagine is having obtained superintelligent AGI-systems that can be imagined as trying to seem aligned/helpful/capable (while not necessarily being aligned/helpful)^[19]. And then I try to ponder: Given that hypothetical situation, what steps might we take from there (if we want to obtain robustly aligned superintelligences while minimizing risk along the way)?

Needs simplification more than clarification.

Thanks for the feedback (and thanks again for engaging). You may well be right about that. For sure I have plenty of room for improvement whereas communication is concerned.

That being said, a suspicion of mine is that it can be easy to significantly underestimate the inferential distance that I have to cover (even if usually correct heuristics make that seem unlikely). I am already leaving out concepts that I myself think of as “core” concepts, so as to reduce length.

1. ^{^}

   Or at least that's one way to think of it.

2. ^{^}

   By “reach” I mean: What is the range of questions where, for at least one answer to that question, there is at least one answer that can be argued for by a high-scoring argument-step-network?

3. ^{^}

   A maximally “non-general” function would have 1 if-sentence per possible input.

   Possible heuristics:

   - “% chance that random change to code chances output for a low but non-zero fraction of inputs”

   - “If there are possible simplifications to the code that would affect a low but non-zero fraction of inputs”

4. ^{^}

   One example of such a heuristic:

   “% of source code that consists of subroutines where no AI is able to make accurate guesses (based on the subroutine input) for what the argument-network argues for/against.

   Values for heuristics could be determined by running zero-sum AI vs AI games (including games where the AIs make bets on what the outcomes of other AI vs AI games would be).

5. ^{^}

   The minimum amount necessary for achieving low wiggle-room (while still having it be possible to construct high-scoring argument-step-networks with useful conclusions).

6. ^{^}

   Similar reasoning to this also applies to maximal source code length. It’s probably hard to minimize source code as much as possible while trying to make it so that conclusions turn out wrong from certain specific questions (but otherwise turn out correct). So AIs that try to do that would presumably lose points to competing AIs that don’t (presuming the AIs are equally competent, etc).

7. ^{^}

   As alluded to earlier, score-functions should not have the expressivity or space that is necessary for predicting human answers from within themselves. And they would therefore have to make system-calls to external systems that make such predictions. And there are restrictions in regards to the system-calls that are allowed.

8. ^{^}

   Based on predictions of what reviewers will answer to meta-questions where they are asked to compare other meta-questions 🙃

9. ^{^}

   Or maybe some other process that approximates such a process (meaning that we do gradient descent, or something like that, as if we had AIs compete against each other in zero-sum games).

10. ^{^}

    If so, the AI that made the “non-general” score-function would lose points (and each point lost by one AI is earned by the other).

11. ^{^}

    By other AI-systems, that are selected for based on their ability to find examples of wiggle-room.

12. ^{^}

    As they for at least one statement would allow for both P and ¬P - and at least one of those has to be bad/wrong.

13. ^{^}

    Or maybe it was wrong to claim that SF1 and SF2 have low internal wiggle-room. But some AIs would be “incentivized” to find counter-examples (that prove claims of low wiggle-room wrong). And if counter-examples are found, then the AI that posited low wiggle-room will be penalized (with the penalty being proportional to the level of confidence that was expressed).

14. ^{^}

    Earlier I talked about score-function restrictions, and how we would define score-function restrictions that score-functions would need to adhere to.

    The simplest thing to imagine is that we define one set of restrictions that score-functions must adhere to (hence it makes sense to start explaining things that way). But what I really envision is that we “explore landscapes” of higher-level wiggle-room relating to “spaces” of possible ways score-functions can be restricted.

    The patterns we see could, in combination with our initial assumptions regarding P(high-scoring arguments can be trusted | score-function has low wiggle-room), provide strong evidence.

    If our assumptions regarding regarding P(high-scoring arguments can be trusted | score-function has low wiggle-room) typically are correct (and at least not way off), then this should be possible to see from the "patterns" that are revealed.

    I could try to explain this better and more concretely if requested. 

15. ^{^}

    If they are the same level of complexity, then there is higher-level wiggle-room (e.g. relative to the score-function requirements / score-function score-function).

16. ^{^}

    Well, not literally anything, but an incomprehensibly large amount of possible statements.

17. ^{^}

    Or score-function score-functions.

18. ^{^}

    Presuming that we have succeeded in getting at least one of the AIs in question to act as if it is trying to maximize points for every individual request that it gets.

19. ^{^}

    While also imagining that these AGI-systems have been successfully contained, are extremely limited in terms of their communication to operators, etc.