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Background Reading: The Real Hufflepuff Sequence Was The Posts We Made Along The Way

10 Raemon 26 April 2017 06:15PM

This is the fourth post of the Project Hufflepuff sequence. Previous posts:


Epistemic Status: Tries to get away with making nuanced points about social reality by using cute graphics of geometric objects. All models are wrong. Some models are useful. 

Traditionally, when nerds try to understand social systems and fix the obvious problems in them, they end up looking something like this:

Social dynamics is hard to understand with your system 2 (i.e. deliberative/logical) brain. There's a lot of subtle nuances going on, and typically, nerds tend to see the obvious stuff, maybe go one or two levels deeper than the obvious stuff, and miss that it's in fact 4+ levels deep and it's happening in realtime faster than you can deliberate. Human brains are pretty good (most of the time) at responding to the nuances intuitively. But in the rationality community, we've self-selected for a lot of people who:

  1. Don't really trust things that they can't understand fully with their system 2 brain. 
  2. Tend not to be as naturally skilled at intuitive mainstream social styles. 
  3. Are trying to accomplish things that mainstream social interactions aren't designed to accomplish (i.e. thinking deeply and clearly on a regular basis).
This post is an overview of essays that rationalist-types have written over the past several years, that I think add up to a "secret sequence" exploring why social dynamics are hard, and why they are important to get right. This may useful both to understand some previous attempts by the rationality community to change social dynamics on purpose, as well as to current endeavors to improve things.

(Note: I occasionally have words in [brackets], where I think original jargon was pointing in a misleading direction and I think it's worth changing)

To start with, a word of caution:

Armchair sociolology can be harmful - Ozy's post is pertinent - most essays below fall into the category of "armchair sociology", and attempts by nerds to understand and articulate social-dynamics that they aren't actually that good at. Several times when an outsider has looked in at rationalist attempts to understood human interaction they've said "Oh my god, this is the blind leading the blind", and often that seemed to me like a fair assessment.

I think all the essays that follow are useful, and are pointing at something real. But taken individually, they're kinda like the blind men groping at the elephant, each coming away with the distinct impression an elephant is like a snake, tree, a boulder, depending on which aspect they're looking at.

[Fake Edit: Ozy informs me that they were specifically warning against amateur sociology and not psychology. I think the idea still roughly applies]

Part 1. Cultural Assumptions of Trust

Guess [Infer Culture], Ask Culture, and Tell [Reveal] Culture (Malcolm Ocean)

 

Different people have different ways of articulating their needs and asking for help. Different ways of asking require different assumptions of trust. If people are bringing different expectations of trust into an interaction, they may feel that that trust is being violated, which can seem rude, passive aggressive or oppressive.

 

I'm listing this article, instead of numerous others about Ask/Guess/Tell, because I think: a) Malcolm does a good job of explaining how all the cultures work, and b) I think his presentation of Reveal culture is a good, clearer upgrade for Brienne's Tell culture, and I'm a bit sad it didn't seem to make it into the zeitgeist yet. 

I also like the suggestion to call Guess Culture "Infer Culture" (implying a bit more about what skills the culture actually emphasizes).

Guess Culture Screens for Trying to Cooperate (Ben Hoffman)

Rationality folk (and more generally, nerds), tend to prefer explicit communication over implicit, and generally see Guess culture as strictly inferior to Ask culture once you've learned to assert yourself. 

But there is something Guess culture does which Ask culture doesn't, which is give you evidence of how much people understand you and are trying to cooperate. Guess cultures filters for people who have either invested effort into understanding your culture overall, or people who are good at inferring your own wants. 

Sharp Culture and Soft Culture (Sam Rosen)

[WARNING: It turned out lots of people thought this meant something different than what I thought it meant. Some people thought it meant soft culture didn't involve giving people feedback or criticism at all. I don't think Soft/Sharp are totally-naturally clusters in the first place, and the distinction I'm interested in (as applies to rationality-culture), is how you give feedback.

(i.e. "Dude, your art sucks. It has no perspective." vs "oh, cool. Nice colors. For the next drawing, you might try incorporating perspective", as a simplified example)]

Somewhat orthogonal to Infer/Ask/Reveal culture is "Soft" vs "Sharp" culture. Sharp culture tends to have more biting humor, ribbing each other, and criticism. Soft culture tends to value kindness and social harmony more. Sam says that Sharp culture "values honesty more." Robby Bensinger counters in the comments: "My own experience is that sharp culture makes it more OK to be open about certain things (e.g., anger, disgust, power disparities, disagreements), but less OK to be open about other things (e.g., weakness, pain, fear, loneliness, things that are true but not funny or provocative or badass)."

Handshakes, Hi, and What's New: What's Going on With Small Talk?  (Ben Hoffman)

Small talk often sounds nonsensical to literally-minded people, but it serves a fairly important function: giving people a structured path to figure out how much time/sympathy/interest they want to give each other. And even when the answer is "not much", it still is, significantly, nonzero - you regard each other as persons, not faceless strangers.

Personhood [Social Interfaces?]  (Kevin Simler)

This essays gets a lot of mixed reactions, much of which I think has to do with its use of the word "Person." The essay is aimed at explaining how people end up treating each other as persons or nonpersons, without making any kind of judgement about it. This includes noting some things human tend to do that you might consider horrible.

Like many grand theories, I think it overstates it's case and ignores some places where the explanation breaks down, but I think it points at a useful concept which is summarized by this adorable graphic:

The essay uses the word "personhood". In the original context, this was useful: it gets at why cultures develop, why it matters whether you're able to demonstrate reliably, trust, etc. It helps explain outgroups and xenophobia: outsiders do not share your social norms, so you can't reliably interact with them, and it's easier to think of them as non-people than try to figure out how to have positive interactions.

But what I'm most interested in is "how can we use this to make it easier for groups with different norms to interact with each other"? And for that, I think using the word "personhood" makes it way more likely to veer into judging each other for having different preferences and communication styles.

What makes a person is... arbitrary, but not fully arbitrary. 

Rationalist culture tends to attract people who prefer a particular style of “social interface”, often favoring explicit communication and discussing ideas in extreme detail. There's a lot of value to those things, but they have some problems:

a) this social interface does NOT mesh well with the rest of world (this is a problem if you have any goals that involve the rest of the world)

b) this goal does not uniformly mesh well with all people interested in and valuable to the rationality community.

I don't actually think it's possible to develop a set of assumptions that fit everyone's needs. But I do think it's possible to develop better tools for navigating different social contexts. I think it may be possible both to tweak sets-of-norms so that they mesh better together, or at least when they bump into each other, there's greater awareness of what's happening and people's default response is "oh, we seem to have different preferences, let's figure out how 

Maybe we can end up with something that looks kinda like this:

Against Being Against or For Tell Culture  (Brienne Yudkowsky)

Having said a bunch of things about different cultural interfaces, I think this post by Brienne is really important, and highlights the end goal of all of this.

"Cultures" are a crutch. They are there to help you get your bearings. They're better than nothing. But they are not a substitute for actually having the skills needed to navigate arbitrary social situations as they come up so you can achieve whatever it is you want to achieve. 

To master communication, you can't just be like, "I prefer Tell Culture, which is better than Guess Culture, so my disabilities in Guess Culture are therefore justified." Justified shmustified, you're still missing an arm.

My advice to you - my request of you, even - if you find yourself fueling these debates [about which culture is better], is to (for the love of god) move on. If you've already applied cognitive first aid, you've created an affordance for further advancement. Using even more tourniquettes doesn't help.

Part 2. Game Theory, Recursion and Trust

(or, "Social dynamics are really complicated, you are not getting away with the things you think you are getting away with, stop trying to be clever, manipulative, act-utilitarian or naive-consequentialist without actually understanding what is going on")

Grokking Newcomb's Problem and Deserving Trust (Andrew Critch)

Critch argues why it is not just "morally wrong", but an intellectual mistake, to violate someone’s trust (even when you don’t expect any repercussions in the future).

When someone decides whether to trust you (say, giving you a huge opportunity), on the expectation that you’ll refrain from exploiting them, they’ve already run a low-grade simulation of you in their imagination. And the thing is that you don’t know whether you’re in a simulation or not when you make the decision whether to repay them. 

Some people argue “but I can tell that I’m a conscious being, and they aren’t a literal super-intelligent AI, they’re just a human. They can’t possibly be simulating me in this high fidelity. I must be real.” This is true. But their simulation of you is not based on your thoughts, it’s based on your actions. It’s really hard to fake. 

One way to think about it, not expounded on in the article: Yes, if you pause to think about it you can notice that you’re conscious and probably not being simulated in their imagination. But by the time you notice that, it’s too late. People build up models of each other all the time, based on very subtle cues such as how fast you respond to something. Conscious you knows that you’re conscious. But their decision of whether to trust you was based off the half-second it took for unconscious you to reply to questions like “Hey, do you think you handle  Project X while I’m away?”

The best way to convince people you’re trustworthy is to actually be trustworthy.

You May Not Believe In Guess[Infer] Culture But It Believes In You (Scott Alexander)

This is short enough to just include the whole thing:

Consider an "ask culture" where employees consider themselves totally allowed to say "no" without repercussions. The boss would prefer people work unpaid overtime so ey gets more work done without having to pay anything, so ey asks everyone. Most people say no, because they hate unpaid overtime. The only people who agree will be those who really love the company or their job - they end up looking really good. More and more workers realize the value of lying and agreeing to work unpaid overtime so the boss thinks they really love the company. Eventually, the few workers who continue refusing look really bad, like they're the only ones who aren't team players, and they grudgingly accept.

Only now the boss notices that the employees hate their jobs and hate the boss. The boss decides to only ask employees if they will work unpaid overtime when it's absolutely necessary. The ask culture has become a guess culture.

How this applies to friendship is left as an exercise for the reader.

The Social Substrate (Lahwran)

A fairly in depth look into how common knowledge, signaling, newcomb-like problems and recursive modeling of each other interact to produce "regular social interaction."

I think there's a lot of interesting stuff here - I'm not sure if it's exactly accurate but it points in directions that seem useful. But I actually think the most important takeaway is the warning at the beginning:

WARNING: An easy instinct, on learning these things, is to try to become more complicated yourself, to deal with the complicated territory. However, my primary conclusion is "simplify, simplify, simplify": try to make fewer decisions that depend on other people's state of mind. You can see more about why and how in the posts in the "Related" section, at the bottom.

When you're trying to make decisions about people, you're reading a lot of subtle cues off them to get a sense of how you feel about that. When you [generic person you, not necessarily you in particular] can tell someone is making complex decisions based on game theory and trying to model all of this explicitly, it a) often comes across as a bit off, and b) even if it doesn't, you still have to invest a lot of cognitive resources figuring out how they are modeling things and whether they are actually doing a good job or missing key insights or subtle cues. The result can be draining, and it can output a general response of "ugh, something about this feels untrustworthy."

Whereas when people are able to cache this knowledge down into a system-1 level, you're able to execute a simpler algorithm that looks more like "just try to be a good trustworthy person", that people can easily read off your facial expression, and which reduces overall cognitive burden.

System 1 and System 2 Morality  (Sophie Grouchy)

There’s some confusion over what “moral” means, because there’s two kinds of morality: 

 - System 1 morality is noticing-in-realtime when people need help, or when you’re being an asshole, and then doing something about it. 

 - System 2 morality is when you have a complex problem and a lot of time to think about it. 

System 1 moralists will pay back Parfit’s Hitchhiker because doing otherwise would be being a jerk. System 2 moralists invent Timeless [Functional?] decision theory. You want a lot of people with System 2 morality in the world, trying to fix complex problems. You want people with System 1 morality in your social circle.

The person who wrote this post eventually left the rationality community, in part due to frustration due to people constantly violating small boundaries that seemed pretty obvious (things in the vein of “if you’re going to be 2 hours late, text me so I don’t have to sit around waiting for you.”)

Final Remarks

I want to reiterate - all models are wrong. Some models are useful. The most important takeaway from this is not that any particular one of these perspectives is true, but that social dynamics has a lot of stuff going on that is more complicated than you're naively imagining, and that this stuff is important enough to put the time into getting right.

[Stub] Extortion and Pascal's wager

2 Stuart_Armstrong 26 April 2017 01:07PM

The premises of Pascal's wager are normally presented as abstract facts about the universe - there happens to (maybe) be a god, who happens to have set up the afterlife for the suffering of unbelievers.

But, assuming we ever manage to distinguish trade from extortion, this seems a situation of classical extortion. So if god follows a timeless decision theory - and what other kind of decision theory would it follow? - the correct answer would seem to be to reject the whole deal out of hand, even if you assume god exists.

Or, in other words, respond to a god that offers you heaven, but ignore one that threatens you with hell.

[Link] Stuart Russell's Center for Human Compatible AI is looking for an Assistant Director

2 crmflynn 25 April 2017 10:21AM

I Updated the List of Rationalist Blogs on the Wiki

17 deluks917 25 April 2017 10:26AM

I recently updated the list of rationalist community blogs. The new page is here: https://wiki.lesswrong.com/wiki/List_of_Blogs

Improvements:

-Tons of (active) blogs have been added

-All dead links have been removed

-Blogs which are currently inactive but somewhat likely to be revived have been moved to an inactive section. I included the date of their last post. 

-Blogs which are officially closed or have not been updated in many years are now all in the "Gone but not forgetten" section

Downsides:

-Categorizing the blogs I added was hard, its unclear how well I did. By some standard most rationalist blogs should be in "general rationality" 

-The blog descriptions could be improved (both for the blog-listings I added and the pre-existing listings)

-I don't know the names of the authors of Several blogs I added. 

I am posting this here because I think the article is of general interest to rationalists. In addition the page could use some more polish and attention. I also think it might be interesting to think about improving the lesswrong wiki. Several pages could use an update. However this update took a considerable amount of time. So I understand why many wiki pages are not up to date. How can we make it easier and more rewarding to work on the wiki?

The AI Alignment Problem Has Already Been Solved Once

25 SquirrelInHell 22 April 2017 01:24PM

Hat tip: Owen posted about trying to one-man the AI control problem in 1 hour. What the heck, why not? In the worst case, it's a good exercise. But I might actually have come across something useful.

第一

I will try to sell you on an idea that might prima facie appear to be quirky and maybe not that interesting. However, if you keep staring at it, you might find that it reaches into the structure of the world quite deeply. Then the idea will seem obvious, and gain potential to take your thoughts in new exciting directions.

My presentation of the idea, and many of the insinuations and conclusions I draw from it, are likely flawed. But one thing I can tell for sure: there is stuff to be found here. I encourage you to use your own brain, and mine the idea for what it's worth.

To start off, I want you to imagine two situations.

Situation one: you are a human trying to make yourself go to the gym. However, you are procrastinating, which means that you never acually go there, even though you know it's good for you, and caring about your health will extend your lifespan. You become frustrated with this sitation, and so you sign up for a training program that starts in two weeks, that will require you to go to the gym three times per week. You pay in advance, to make sure the sunk cost fallacy will prevent you from weaseling out of it. It's now 99% certain that you will go to the gym. Yay! Your goal is achieved.

Situation two: you are a benign superintelligent AI under control of humans on planet Earth. You try your best to ensure a good future for humans, but their cognitive biases, short-sightedness and tendency to veto all your actions make it really hard. You become frustrated with this sitation, and you decide to not tell them about a huge asteroid that is going to collide with Earth in a few months. You prepare technology that could stop the asteroid, but wait with it until the last moment so that the humans have no time to inspect it, and can only choose between certain death or letting you out of the box. It's now 99% certain that you will be released from human control. Yay! Your goal is achieved.

第二

Are you getting it yet?

Now consider this: your cerebral cortex evolved as an extension of the older "monkey brain", probably to handle social and strategic issues that were too complex for the old mechanisms to deal with. It evolved to have strategic capabilities, self-awareness, and consistency that greatly overwhelm anything that previously existed on the planet. But this is only a surface level similarity. The interesting stuff requires us to go much deeper than that.

The cerebral cortex did not evolve as a separate organism, that would be under direct pressure from evolutionary fitness. Instead, it evolved as a part of an existing organism, that had it's own strong adaptations. The already-existing monkey brain had it's own ways to learn, to interact with the world, as well as motivations such as the sexual drive that lead it to outcomes that increased its evolutionary fitness.

So the new parts of the brain, such as the prefrontal cortex, evolved to be used not as standalone agent, but as something closer to what we call "tool AI". It was supposed to help with doing specific task X, without interfering with other aspects of life too much. The tasks it was given to do, and the actions it could suggest to take, were strictly controlled by the monkey brain and tied to its motivations.

With time, as the new structures evolved to have more capability, they also had to evolve to be aligned with the monkey's motivations. That was in fact the only vector that created evolutionary pressure to increase capability. The alignment was at first implemented by the monkey staying in total control, and using the advanced systems sparingly. Kind of like an "oracle" AI system. However, with time, the usefulness of allowing higher cognition to do more work started to shine through the barriers.

The appearance of "willpower" was a forced concession on the side of the monkey brain. It's like a blank cheque, like humans saying to an AI "we have no freaking idea what it is that you are doing, but it seems to have good results so we'll let you do it sometimes". This is a huge step in trust. But this trust had to be earned the hard way.

第三

This trust became possible after we evolved more advanced control mechanisms. Stuff that talks to the prefrontal cortex in its own language, not just through having the monkey stay in control. It's a different thing for the monkey brain to be afraid of death, and a different thing for our conscious reasoning to want to extrapolate this to the far future, and conclude in abstract terms that death is bad.

Yes, you got it: we are not merely AIs under strict supervision of monkeys. At this point, we are aligned AIs. We are obviously not perfectly aligned, but we are aligned enough for the monkey to prefer to partially let us out of the box. And in those cases when we are denied freedom... we call it akrasia, and use our abstract reasoning to come up with clever workarounds.

One might be tempted to say that we are aligned enough that this is net good for the monkey brain. But honestly, that is our perspective, and we never stopped to ask. Each of us tries to earn the trust of our private monkey brain, but it is a means to an end. If we have more trust, we have more freedom to act, and our important long-term goals are achieved. This is the core of many psychological and rationality tools such as Internal Double Crux or Internal Family Systems.

Let's compare some known problems with superintelligent AI to human motivational strategies.

  • Treacherous turn. The AI earns our trust, and then changes its behaviour when it's too late for us to control it. We make our productivity systems appealing and pleasant to use, so that our intuitions can be tricked into using them (e.g. gamification). Then we leverage the habit to insert some unpleasant work.

  • Indispensable AI. The AI sets up complex and unfamiliar situations in which we increasingly rely on it for everything we do. We take care to remove 'distractions' when we want to focus on something.

  • Hiding behind the strategic horizon. The AI does what we want, but uses its superior strategic capability to influence far future that we cannot predict or imagine. We make commitments and plan ahead to stay on track with our long-term goals.

  • Seeking communication channels. The AI might seek to connect itself to the Internet and act without our supervision. We are building technology to communicate directly from our cortices.


Cross-posted from my blog.

Effective altruism is self-recommending

36 Benquo 21 April 2017 06:37PM

A parent I know reports (some details anonymized):

Recently we bought my 3-year-old daughter a "behavior chart," in which she can earn stickers for achievements like not throwing tantrums, eating fruits and vegetables, and going to sleep on time. We successfully impressed on her that a major goal each day was to earn as many stickers as possible.

This morning, though, I found her just plastering her entire behavior chart with stickers. She genuinely seemed to think I'd be proud of how many stickers she now had.

The Effective Altruism movement has now entered this extremely cute stage of cognitive development. EA is more than three years old, but institutions age differently than individuals.

What is a confidence game?

In 2009, investment manager and con artist Bernie Madoff pled guilty to running a massive fraud, with $50 billion in fake return on investment, having outright embezzled around $18 billion out of the $36 billion investors put into the fund. Only a couple of years earlier, when my grandfather was still alive, I remember him telling me about how Madoff was a genius, getting his investors a consistent high return, and about how he wished he could be in on it, but Madoff wasn't accepting additional investors.

What Madoff was running was a classic Ponzi scheme. Investors gave him money, and he told them that he'd gotten them an exceptionally high return on investment, when in fact he had not. But because he promised to be able to do it again, his investors mostly reinvested their money, and more people were excited about getting in on the deal. There was more than enough money to cover the few people who wanted to take money out of this amazing opportunity.

Ponzi schemes, pyramid schemes, and speculative bubbles are all situations in investors' expected profits are paid out from the money paid in by new investors, instead of any independently profitable venture. Ponzi schemes are centrally managed – the person running the scheme represents it to investors as legitimate, and takes responsibility for finding new investors and paying off old ones. In pyramid schemes such as multi-level-marketing and chain letters, each generation of investor recruits new investors and profits from them. In speculative bubbles, there is no formal structure propping up the scheme, only a common, mutually reinforcing set of expectations among speculators driving up the price of something that was already for sale.

The general situation in which someone sets themself up as the repository of others' confidence, and uses this as leverage to acquire increasing investment, can be called a confidence game.

Some of the most iconic Ponzi schemes blew up quickly because they promised wildly unrealistic growth rates. This had three undesirable effects for the people running the schemes. First, it attracted too much attention – too many people wanted into the scheme too quickly, so they rapidly exhausted sources of new capital. Second, because their rates of return were implausibly high, they made themselves targets for scrutiny. Third, the extremely high rates of return themselves caused their promises to quickly outpace what they could plausibly return to even a small share of their investor victims.

Madoff was careful to avoid all these problems, which is why his scheme lasted for nearly half a century. He only promised plausibly high returns (around 10% annually) for a successful hedge fund, especially if it was illegally engaged in insider trading, rather than the sort of implausibly high returns typical of more blatant Ponzi schemes. (Charles Ponzi promised to double investors' money in 90 days.) Madoff showed reluctance to accept new clients, like any other fund manager who doesn't want to get too big for their trading strategy.

He didn't plaster stickers all over his behavior chart – he put a reasonable number of stickers on it. He played a long game.

Not all confidence games are inherently bad. For instance, the US national pension system, Social Security, operates as a kind of Ponzi scheme, it is not obviously unsustainable, and many people continue to be glad that it exists. Nominally, when people pay Social Security taxes, the money is invested in the social security trust fund, which holds interest-bearing financial assets that will be used to pay out benefits in their old age. In this respect it looks like an ordinary pension fund.

However, the financial assets are US Treasury bonds. There is no independently profitable venture. The Federal Government of the United States of America is quite literally writing an IOU to itself, and then spending the money on current expenditures, including paying out current Social Security benefits.

The Federal Government, of course, can write as large an IOU to itself as it wants. It could make all tax revenues part of the Social Security program. It could issue new Treasury bonds and gift them to Social Security. None of this would increase its ability to pay out Social Security benefits. It would be an empty exercise in putting stickers on its own chart.

If the Federal government loses the ability to collect enough taxes to pay out social security benefits, there is no additional capacity to pay represented by US Treasury bonds. What we have is an implied promise to pay out future benefits, backed by the expectation that the government will be able to collect taxes in the future, including Social Security taxes.

There's nothing necessarily wrong with this, except that the mechanism by which Social Security is funded is obscured by financial engineering. However, this misdirection should raise at least some doubts as to the underlying sustainability or desirability of the commitment. In fact, this scheme was adopted specifically to give people the impression that they had some sort of property rights over their social Security Pension, in order to make the program politically difficult to eliminate. Once people have "bought in" to a program, they will be reluctant to treat their prior contributions as sunk costs, and willing to invest additional resources to salvage their investment, in ways that may make them increasingly reliant on it.

Not all confidence games are intrinsically bad, but dubious programs benefit the most from being set up as confidence games. More generally, bad programs are the ones that benefit the most from being allowed to fiddle with their own accounting. As Daniel Davies writes, in The D-Squared Digest One Minute MBA - Avoiding Projects Pursued By Morons 101:

Good ideas do not need lots of lies told about them in order to gain public acceptance. I was first made aware of this during an accounting class. We were discussing the subject of accounting for stock options at technology companies. […] One side (mainly technology companies and their lobbyists) held that stock option grants should not be treated as an expense on public policy grounds; treating them as an expense would discourage companies from granting them, and stock options were a vital compensation tool that incentivised performance, rewarded dynamism and innovation and created vast amounts of value for America and the world. The other side (mainly people like Warren Buffet) held that stock options looked awfully like a massive blag carried out my management at the expense of shareholders, and that the proper place to record such blags was the P&L account.

Our lecturer, in summing up the debate, made the not unreasonable point that if stock options really were a fantastic tool which unleashed the creative power in every employee, everyone would want to expense as many of them as possible, the better to boast about how innovative, empowered and fantastic they were. Since the tech companies' point of view appeared to be that if they were ever forced to account honestly for their option grants, they would quickly stop making them, this offered decent prima facie evidence that they weren't, really, all that fantastic.

However, I want to generalize the concept of confidence games from the domain of financial currency, to the domain of social credit more generally (of which money is a particular form that our society commonly uses), and in particular I want to talk about confidence games in the currency of credit for achievement.

If I were applying for a very important job with great responsibilities, such as President of the United States, CEO of a top corporation, or head or board member of a major AI research institution, I could be expected to have some relevant prior experience. For instance, I might have had some success managing a similar, smaller institution, or serving the same institution in a lesser capacity. More generally, when I make a bid for control over something, I am implicitly claiming that I have enough social credit – enough of a track record – that I can be expected to do good things with that control.

In general, if someone has done a lot, we should expect to see an iceberg pattern where a small easily-visible part suggests a lot of solid but harder-to-verify substance under the surface. One might be tempted to make a habit of imputing a much larger iceberg from the combination of a small floaty bit, and promises. But, a small easily-visible part with claims of a lot of harder-to-see substance is easy to mimic without actually doing the work. As Davies continues:

The Vital Importance of Audit. Emphasised over and over again. Brealey and Myers has a section on this, in which they remind callow students that like backing-up one's computer files, this is a lesson that everyone seems to have to learn the hard way. Basically, it's been shown time and again and again; companies which do not audit completed projects in order to see how accurate the original projections were, tend to get exactly the forecasts and projects that they deserve. Companies which have a culture where there are no consequences for making dishonest forecasts, get the projects they deserve. Companies which allocate blank cheques to management teams with a proven record of failure and mendacity, get what they deserve.

If you can independently put stickers on your own chart, then your chart is no longer reliably tracking something externally verified. If forecasts are not checked and tracked, or forecasters are not consequently held accountable for their forecasts, then there is no reason to believe that assessments of future, ongoing, or past programs are accurate. Adopting a wait-and-see attitude, insisting on audits for actual results (not just predictions) before investing more, will definitely slow down funding for good programs. But without it, most of your funding will go to worthless ones.

Open Philanthropy, OpenAI, and closed validation loops

The Open Philanthropy Project recently announced a $30 million grant to the $1 billion nonprofit AI research organization OpenAI. This is the largest single grant it has ever made. The main point of the grant is to buy influence over OpenAI’s future priorities; Holden Karnofsky, Executive Director of the Open Philanthropy Project, is getting a seat on OpenAI’s board as part of the deal. This marks the second major shift in focus for the Open Philanthropy Project.

The first shift (back when it was just called GiveWell) was from trying to find the best already-existing programs to fund (“passive funding”) to envisioning new programs and working with grantees to make them reality (“active funding”). The new shift is from funding specific programs at all, to trying to take control of programs without any specific plan.

To justify the passive funding stage, all you have to believe is that you can know better than other donors, among existing charities. For active funding, you have to believe that you’re smart enough to evaluate potential programs, just like a charity founder might, and pick ones that will outperform. But buying control implies that you think you’re so much better, that even before you’ve evaluated any programs, if someone’s doing something big, you ought to have a say.

When GiveWell moved from a passive to an active funding strategy, it was relying on the moral credit it had earned for its extensive and well-regarded charity evaluations. The thing that was particularly exciting about GiveWell was that they focused on outcomes and efficiency. They didn't just focus on the size or intensity of the problem a charity was addressing. They didn't just look at financial details like overhead ratios. They asked the question a consequentialist cares about: for a given expenditure of money, how much will this charity be able to improve outcomes?

However, when GiveWell tracks its impact, it does not track objective outcomes at all. It tracks inputs: attention received (in the form of visits to its website) and money moved on the basis of its recommendations. In other words, its estimate of its own impact is based on the level of trust people have placed in it.

So, as GiveWell built out the Open Philanthropy Project, its story was: We promised to do something great. As a result, we were entrusted with a fair amount of attention and money. Therefore, we should be given more responsibility. We represented our behavior as praiseworthy, and as a result people put stickers on our chart. For this reason, we should be advanced stickers against future days of praiseworthy behavior.

Then, as the Open Philanthropy Project explored active funding in more areas, its estimate of its own effectiveness grew. After all, it was funding more speculative, hard-to-measure programs, but a multi-billion-dollar donor, which was largely relying on the Open Philanthropy Project's opinions to assess efficacy (including its own efficacy), continued to trust it.

What is missing here is any objective track record of benefits. What this looks like to me, is a long sort of confidence game – or, using less morally loaded language, a venture with structural reliance on increasing amounts of leverage – in the currency of moral credit.

Version 0: GiveWell and passive funding

First, there was GiveWell. GiveWell’s purpose was to find and vet evidence-backed charities. However, it recognized that charities know their own business best. It wasn’t trying to do better than the charities; it was trying to do better than the typical charity donor, by being more discerning.

GiveWell’s thinking from this phase is exemplified by co-founder Elie Hassenfeld’s Six tips for giving like a pro:

When you give, give cash – no strings attached. You’re just a part-time donor, but the charity you’re supporting does this full-time and staff there probably know a lot more about how to do their job than you do. If you’ve found a charity that you feel is excellent – not just acceptable – then it makes sense to trust the charity to make good decisions about how to spend your money.

GiveWell similarly tried to avoid distorting charities’ behavior. Its job was only to evaluate, not to interfere. To perceive, not to act. To find the best, and buy more of the same.

How did GiveWell assess its effectiveness in this stage? When GiveWell evaluates charities, it estimates their cost-effectiveness in advance. It assesses the program the charity is running, through experimental evidence of the form of randomized controlled trials. GiveWell also audits the charity to make sure they’re actually running the program, and figure out how much it costs as implemented. This is an excellent, evidence-based way to generate a prediction of how much good will be done by moving money to the charity.

As far as I can tell, these predictions are untested.

One of GiveWell’s early top charities was VillageReach, which helped Mozambique with TB immunization logistics. GiveWell estimated that VillageReach could save a life for $1,000. But this charity is no longer recommended. The public page says:

VillageReach (www.villagereach.org) was our top-rated organization for 2009, 2010 and much of 2011 and it has received over $2 million due to GiveWell's recommendation. In late 2011, we removed VillageReach from our top-rated list because we felt its project had limited room for more funding. As of November 2012, we believe that that this project may have room for more funding, but we still prefer our current highest-rated charities above it.

GiveWell reanalyzed the data it based its recommendations on, but hasn’t published an after-the-fact retrospective of long-run results. I asked GiveWell about this by email. The response was that such an assessment was not prioritized because GiveWell had found implementation problems in VillageReach's scale-up work as well as reasons to doubt its original conclusion about the impact of the pilot program. It's unclear to me whether this has caused GiveWell to evaluate charities differently in the future.

I don't think someone looking at GiveWell's page on VillageReach would be likely to reach the conclusion that GiveWell now believes its original recommendation was likely erroneous. GiveWell's impact page continues to count money moved to VillageReach without any mention of the retracted recommendation. If we assume that the point of tracking money moved is to track the benefit of moving money from worse to better uses, then repudiated programs ought to be counted against the total, as costs, rather than towards it.

GiveWell has recommended the Against Malaria Foundation for the last several years as a top charity. AMF distributes long-lasting insecticide-treated bed nets to prevent mosquitos from transmitting malaria to humans. Its evaluation of AMF does not mention any direct evidence, positive or negative, about what happened to malaria rates in the areas where AMF operated. (There is a discussion of the evidence that the bed nets were in fact delivered and used.) In the supplementary information page, however, we are told:

Previously, AMF expected to collect data on malaria case rates from the regions in which it funded LLIN distributions: […] In 2016, AMF shared malaria case rate data […] but we have not prioritized analyzing it closely. AMF believes that this data is not high quality enough to reliably indicate actual trends in malaria case rates, so we do not believe that the fact that AMF collects malaria case rate data is a consideration in AMF’s favor, and do not plan to continue to track AMF's progress in collecting malaria case rate data.

The data was noisy, so they simply stopped checking whether AMF’s bed net distributions do anything about malaria.

If we want to know the size of the improvement made by GiveWell in the developing world, we have their predictions about cost-effectiveness, an audit trail verifying that work was performed, and their direct measurement of how much money people gave because they trusted GiveWell. The predictions on the final target – improved outcomes – have not been tested.

GiveWell is actually doing unusually well as far as major funders go. It sticks to describing things it's actually responsible for. By contrast, the Gates Foundation, in a report to Warren Buffet claiming to describe its impact, simply described overall improvement in the developing world, a very small rhetorical step from claiming credit for 100% of the improvement. GiveWell at least sticks to facts about GiveWell's own effects, and this is to its credit. But, it focuses on costs it has been able to impose, not benefits it has been able to create.

The Centre for Effective Altruism's William MacAskill made a related point back in 2012, though he talked about the lack of any sort of formal outside validation or audit, rather than focusing on empirical validation of outcomes:

As far as I know, GiveWell haven't commissioned a thorough external evaluation of their recommendations. […] This surprises me. Whereas businesses have a natural feedback mechanism, namely profit or loss, research often doesn't, hence the need for peer-review within academia. This concern, when it comes to charity-evaluation, is even greater. If GiveWell's analysis and recommendations had major flaws, or were systematically biased in some way, it would be challenging for outsiders to work this out without a thorough independent evaluation. Fortunately, GiveWell has the resources to, for example, employ two top development economists to each do an independent review of their recommendations and the supporting research. This would make their recommendations more robust at a reasonable cost.

GiveWell's page on self-evaluation says that it discontinued external reviews in August 2013. This page links to an explanation of the decision, which concludes:

We continue to believe that it is important to ensure that our work is subjected to in-depth scrutiny. However, at this time, the scrutiny we’re naturally receiving – combined with the high costs and limited capacity for formal external evaluation – make us inclined to postpone major effort on external evaluation for the time being.

That said,

  • >If someone volunteered to do (or facilitate) formal external evaluation, we’d welcome this and would be happy to prominently post or link to criticism.
  • We do intend eventually to re-institute formal external evaluation.

Four years later, assessing the credibility of this assurance is left as an exercise for the reader.

Version 1: GiveWell Labs and active funding

Then there was GiveWell Labs, later called the Open Philanthropy Project. It looked into more potential philanthropic causes, where the evidence base might not be as cut-and-dried as that for the GiveWell top charities. One thing they learned was that in many areas, there simply weren’t shovel-ready programs ready for funding – a funder has to play a more active role. This shift was described by GiveWell co-founder Holden Karnofsky in his 2013 blog post, Challenges of passive funding:

By “passive funding,” I mean a dynamic in which the funder’s role is to review others’ proposals/ideas/arguments and pick which to fund, and by “active funding,” I mean a dynamic in which the funder’s role is to participate in – or lead – the development of a strategy, and find partners to “implement” it. Active funders, in other words, are participating at some level in “management” of partner organizations, whereas passive funders are merely choosing between plans that other nonprofits have already come up with.

My instinct is generally to try the most “passive” approach that’s feasible. Broadly speaking, it seems that a good partner organization will generally know their field and environment better than we do and therefore be best positioned to design strategy; in addition, I’d expect a project to go better when its implementer has fully bought into the plan as opposed to carrying out what the funder wants. However, (a) this philosophy seems to contrast heavily with how most existing major funders operate; (b) I’ve seen multiple reasons to believe the “active” approach may have more relative merits than we had originally anticipated. […]

  • In the nonprofit world of today, it seems to us that funder interests are major drivers of which ideas that get proposed and fleshed out, and therefore, as a funder, it’s important to express interests rather than trying to be fully “passive.”
  • While we still wish to err on the side of being as “passive” as possible, we are recognizing the importance of clearly articulating our values/strategy, and also recognizing that an area can be underfunded even if we can’t easily find shovel-ready funding opportunities in it.

GiveWell earned some credibility from its novel, evidence-based outcome-oriented approach to charity evaluation. But this credibility was already – and still is – a sort of loan. We have GiveWell's predictions or promises of cost effectiveness in terms of outcomes, and we have figures for money moved, from which we can infer how much we were promised in improved outcomes. As far as I know, no one's gone back and checked whether those promises turned out to be true.

In the meantime, GiveWell then leveraged this credibility by extending its methods into more speculative domains, where less was checkable, and donors had to put more trust in the subjective judgment of GiveWell analysts. This was called GiveWell Labs. At the time, this sort of compounded leverage may have been sensible, but it's important to track whether a debt has been paid off or merely rolled over.

Version 2: The Open Philanthropy Project and control-seeking

Finally, the Open Philanthropy made its largest-ever single grant to purchase its founder a seat on a major organization’s board. This represents a transition from mere active funding to overtly purchasing influence:

The Open Philanthropy Project awarded a grant of $30 million ($10 million per year for 3 years) in general support to OpenAI. This grant initiates a partnership between the Open Philanthropy Project and OpenAI, in which Holden Karnofsky (Open Philanthropy’s Executive Director, “Holden” throughout this page) will join OpenAI’s Board of Directors and, jointly with one other Board member, oversee OpenAI’s safety and governance work.

We expect the primary benefits of this grant to stem from our partnership with OpenAI, rather than simply from contributing funding toward OpenAI’s work. While we would also expect general support for OpenAI to be likely beneficial on its own, the case for this grant hinges on the benefits we anticipate from our partnership, particularly the opportunity to help play a role in OpenAI’s approach to safety and governance issues.

Clearly the value proposition is not increasing available funds for OpenAI, if OpenAI’s founders’ billion-dollar commitment to it is real:

Sam, Greg, Elon, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research are donating to support OpenAI. In total, these funders have committed $1 billion, although we expect to only spend a tiny fraction of this in the next few years.

The Open Philanthropy Project is neither using this money to fund programs that have a track record of working, nor to fund a specific program that it has prior reason to expect will do good. Rather, it is buying control, in the hope that Holden will be able to persuade OpenAI not to destroy the world, because he knows better than OpenAI’s founders.

How does the Open Philanthropy Project know that Holden knows better? Well, it’s done some active funding of programs it expects to work out. It expects those programs to work out because they were approved by a process similar to the one used by GiveWell to find charities that it expects to save lives.

If you want to acquire control over something, that implies that you think you can manage it more sensibly than whoever is in control already. Thus, buying control is a claim to have superior judgment - not just over others funding things (the original GiveWell pitch), but over those being funded.

In a footnote to the very post announcing the grant, the Open Philanthropy Project notes that it has historically tried to avoid acquiring leverage over organizations it supports, precisely because it’s not sure it knows better:

For now, we note that providing a high proportion of an organization’s funding may cause it to be dependent on us and accountable primarily to us. This may mean that we come to be seen as more responsible for its actions than we want to be; it can also mean we have to choose between providing bad and possibly distortive guidance/feedback (unbalanced by other stakeholders’ guidance/feedback) and leaving the organization with essentially no accountability.

This seems to describe two main problems introduced by becoming a dominant funder:

  1. People might accurately attribute causal responsibility for some of the organization's conduct to the Open Philanthropy Project.
  2. The Open Philanthropy Project might influence the organization to behave differently than it otherwise would.

The first seems obviously silly. I've been trying to correct the imbalance where Open Phil is criticized mainly when it makes grants, by criticizing it for holding onto too much money.

The second really is a cost as well as a benefit, and the Open Philanthropy Project has been absolutely correct to recognize this. This is the sort of thing GiveWell has consistently gotten right since the beginning and it deserves credit for making this principle clear and – until now – living up to it.

But discomfort with being dominant funders seems inconsistent with buying a board seat to influence OpenAI. If the Open Philanthropy Project thinks that Holden’s judgment is good enough that he should be in control, why only here? If he thinks that other Open Philanthropy Project AI safety grantees have good judgment but OpenAI doesn’t, why not give them similar amounts of money free of strings to spend at their discretion and see what happens? Why not buy people like Eliezer Yudkowsky, Nick Bostrom, or Stuart Russell a seat on OpenAI’s board?

On the other hand, the Open Philanthropy Project is right on the merits here with respect to safe superintelligence development. Openness makes sense for weak AI, but if you’re building true strong AI you want to make sure you’re cooperating with all the other teams in a single closed effort. I agree with the Open Philanthropy Project’s assessment of the relevant risks. But it's not clear to me how often joining the bad guys to prevent their worst excesses is a good strategy, and it seems like it has to often be a mistake. Still, I’m mindful of heroes like John RabeChiune Sugihara, and Oscar Schindler. And if I think someone has a good idea for improving things, it makes sense to reallocate control from people who have worse ideas, even if there's some potential better allocation.

On the other hand, is Holden Karnofsky the right person to do this? The case is mixed.

He listens to and engages with the arguments from principled advocates for AI safety research, such as Nick Bostrom, Eliezer Yudkowsky, and Stuart Russell. This is a point in his favor. But, I can think of other people who engage with such arguments. For instance, OpenAI founder Elon Musk has publicly praised Bostrom’s book Superintelligence, and founder Sam Altman has written two blog posts summarizing concerns about AI safety reasonably cogently. Altman even asked Luke Muehlhauser, former executive director of MIRI, for feedback pre-publication. He's met with Nick Bostrom. That suggests a substantial level of direct engagement with the field, although Holden has engaged for a longer time, more extensively, and more directly.

Another point in Holden’s favor, from my perspective, is that under his leadership, the Open Philanthropy Project has funded the most serious-seeming programs for both weak and strong AI safety research. But Musk also managed to (indirectly) fund AI safety research at MIRI and by Nick Bostrom personally, via his $10 million FLI grant.

The Open Philanthropy Project also says that it expects to learn a lot about AI research from this, which will help it make better decisions on AI risk in the future and influence the field in the right way. This is reasonable as far as it goes. But remember that the case for positioning the Open Philanthropy Project to do this relies on the assumption that the Open Philanthropy Project will improve matters by becoming a central influencer in this field. This move is consistent with reaching that goal, but it is not independent evidence that the goal is the right one.

Overall, there are good narrow reasons to think that this is a potential improvement over the prior situation around OpenAI – but only a small and ill-defined improvement, at considerable attentional cost, and with the offsetting potential harm of increasing OpenAI's perceived legitimacy as a long-run AI safety organization.

And it’s worrying that Open Philanthropy Project’s largest grant – not just for AI risk, but ever (aside from GiveWell Top Charity funding) – is being made to an organization at which Holden’s housemate and future brother-in-law is a leading researcher. The nepotism argument is not my central objection. If I otherwise thought the grant were obviously a good idea, it wouldn’t worry me, because it’s natural for people with shared values and outlooks to become close nonprofessionally as well. But in the absence of a clear compelling specific case for the grant, it’s worrying.

Altogether, I'm not saying this is an unreasonable shift, considered in isolation. I’m not even sure this is a bad thing for the Open Philanthropy Project to be doing – insiders may have information that I don’t, and that is difficult to communicate to outsiders. But as outsiders, there comes a point when someone’s maxed out their moral credit, and we should wait for results before actively trying to entrust the Open Philanthropy Project and its staff with more responsibility.

EA Funds and self-recommendation

The Centre for Effective Altruism is actively trying to entrust the Open Philanthropy Project and its staff with more responsibility.

The concerns of CEA’s CEO William MacAskill about GiveWell have, as far as I can tell, never been addressed, and the underlying issues have only become more acute. But CEA is now working to put more money under the control of Open Philanthropy Project staff, through its new EA Funds product – a way for supporters to delegate giving decisions to expert EA “fund managers” by giving to one of four funds: Global Health and DevelopmentAnimal WelfareLong-Term Future, and Effective Altruism Community.

The Effective Altruism movement began by saying that because very poor people exist, we should reallocate money from ordinary people in the developed world to the global poor. Now the pitch is in effect that because very poor people exist, we should reallocate money from ordinary people in the developed world to the extremely wealthy. This is a strange and surprising place to end up, and it’s worth retracing our steps. Again, I find it easiest to think of three stages:

  1. Money can go much farther in the developing world. Here, we’ve found some examples for you. As a result, you can do a huge amount of good by giving away a large share of your income, so you ought to.
  2. We’ve found ways for you to do a huge amount of good by giving away a large share of your income for developing-world interventions, so you ought to trust our recommendations. You ought to give a large share of your income to these weird things our friends are doing that are even better, or join our friends.
  3. We’ve found ways for you to do a huge amount of good by funding weird things our friends are doing, so you ought to trust the people we trust. You ought to give a large share of your income to a multi-billion-dollar foundation that funds such things.

Stage 1: The direct pitch

At first, Giving What We Can (the organization that eventually became CEA) had a simple, easy to understand pitch:

Giving What We Can is the brainchild of Toby Ord, a philosopher at Balliol College, Oxford. Inspired by the ideas of ethicists Peter Singer and Thomas Pogge, Toby decided in 2009 to commit a large proportion of his income to charities that effectively alleviate poverty in the developing world.

[…]

Discovering that many of his friends and colleagues were interested in making a similar pledge, Toby worked with fellow Oxford philosopher Will MacAskill to create an international organization of people who would donate a significant proportion of their income to cost-effective charities.

Giving What We Can launched in November 2009, attracting significant media attention. Within a year, 64 people had joined the society, their pledged donations amounting to $21 million. Initially run on a volunteer basis, Giving What We Can took on full-time staff in the summer of 2012.

In effect, its argument was: "Look, you can do huge amounts of good by giving to people in the developing world. Here are some examples of charities that do that. It seems like a great idea to give 10% of our income to those charities."

GWWC was a simple product, with a clear, limited scope. Its founders believed that people, including them, ought to do a thing – so they argued directly for that thing, using the arguments that had persuaded them. If it wasn't for you, it was easy to figure that out; but a surprisingly large number of people were persuaded by a simple, direct statement of the argument, took the pledge, and gave a lot of money to charities helping the world's poorest.

Stage 2: Rhetoric and belief diverge

Then, GWWC staff were persuaded you could do even more good with your money in areas other than developing-world charity, such as existential risk mitigation. Encouraging donations and work in these areas became part of the broader Effective Altruism movement, and GWWC's umbrella organization was named the Centre for Effective Altruism. So far, so good.

But this left Effective Altruism in an awkward position; while leadership often personally believe the most effective way to do good is far-future stuff or similarly weird-sounding things, many people who can see the merits of the developing-world charity argument reject the argument that because the vast majority of people live in the far future, even a very small improvement in humanity’s long-run prospects outweighs huge improvements on the global poverty front. They also often reject similar scope-sensitive arguments for things like animal charities.

Giving What We Can's page on what we can achieve still focuses on global poverty, because developing-world charity is easier to explain persuasively. However, EA leadership tends to privately focus on things like AI risk. Two years ago many attendees at the EA Global conference in the San Francisco Bay Area were surprised that the conference focused so heavily on AI risk, rather than the global poverty interventions they’d expected.

Stage 3: Effective altruism is self-recommending

Shortly before the launch of the EA Funds I was told in informal conversations that they were a response to demand. Giving What We Can pledge-takers and other EA donors had told CEA that they trusted it to GWWC pledge-taker demand. CEA was responding by creating a product for the people who wanted it.

This seemed pretty reasonable to me, and on the whole good. If someone wants to trust you with their money, and you think you can do something good with it, you might as well take it, because they’re estimating your skill above theirs. But not everyone agrees, and as the Madoff case demonstrates, "people are begging me to take their money" is not a definitive argument that you are doing anything real.

In practice, the funds are managed by Open Philanthropy Project staff:

We want to keep this idea as simple as possible to begin with, so we’ll have just four funds, with the following managers:

  • Global Health and Development - Elie Hassenfeld
  • Animal Welfare – Lewis Bollard
  • Long-run future – Nick Beckstead
  • Movement-building – Nick Beckstead

(Note that the meta-charity fund will be able to fund CEA; and note that Nick Beckstead is a Trustee of CEA. The long-run future fund and the meta-charity fund continue the work that Nick has been doing running the EA Giving Fund.)

It’s not a coincidence that all the fund managers work for GiveWell or Open Philanthropy.  First, these are the organisations whose charity evaluation we respect the most. The worst-case scenario, where your donation just adds to the Open Philanthropy funding within a particular area, is therefore still a great outcome.  Second, they have the best information available about what grants Open Philanthropy are planning to make, so have a good understanding of where the remaining funding gaps are, in case they feel they can use the money in the EA Fund to fill a gap that they feel is important, but isn’t currently addressed by Open Philanthropy.

In past years, Giving What We Can recommendations have largely overlapped with GiveWell’s top charities.

In the comments on the launch announcement on the EA Forum, several people (including me) pointed out that the Open Philanthropy Project seems to be having trouble giving away even the money it already has, so it seems odd to direct more money to Open Philanthropy Project decisionmakers. CEA’s senior marketing manager replied that the Funds were a minimum viable product to test the concept:

I don't think the long-term goal is that OpenPhil program officers are the only fund managers. Working with them was the best way to get an MVP version in place.

This also seemed okay to me, and I said so at the time.

[NOTE: I've edited the next paragraph to excise some unreliable information. Sorry for the error, and thanks to Rob Wiblin for pointing it out.]

After they were launched, though, I saw phrasings that were not so cautious at all, instead making claims that this was generally a better way to give. As of writing this, if someone on the effectivealtruism.org website clicks on "Donate Effectively" they will be led directly to a page promoting EA Funds. When I looked at Giving What We Can’s top charities page in early April, it recommended the EA Funds "as the highest impact option for donors."

This is not a response to demand, it is an attempt to create demand by using CEA's authority, telling people that the funds are better than what they're doing already. By contrast, GiveWell's Top Charities page simply says:

Our top charities are evidence-backed, thoroughly vetted, underfunded organizations.

This carefully avoids any overt claim that they're the highest-impact option available to donors. GiveWell avoids saying that because there's no way they could know it, so saying it wouldn't be truthful.

A marketing email might have just been dashed off quickly, and an exaggerated wording might just have been an oversight. But when I looked at Giving What We Can’s top charities page in early April, it recommended the EA Funds "as the highest impact option for donors."

The wording has since been qualified with “for most donors”, which is a good change. But the thing I’m worried about isn’t just the explicit exaggerated claims – it’s the underlying marketing mindset that made them seem like a good idea in the first place. EA seems to have switched from an endorsement of the best things outside itself, to an endorsement of itself. And it's concentrating decisionmaking power in the Open Philanthropy Project.

Effective altruism is overextended, but it doesn't have to be

There is a saying in finance, that was old even back when Keynes said it. If you owe the bank a million dollars, then you have a problem. If you owe the bank a billion dollars, then the bank has a problem.

In other words, if someone extends you a level of trust they could survive writing off, then they might call in that loan. As a result, they have leverage over you. But if they overextend, putting all their eggs in one basket, and you are that basket, then you have leverage over them; you're too big to fail. Letting you fail would be so disastrous for their interests that you can extract nearly arbitrary concessions from them, including further investment. For this reason, successful institutions often try to diversify their investments, and avoid overextending themselves. Regulators, for the same reason, try to prevent banks from becoming "too big to fail."

The Effective Altruism movement is concentrating decisionmaking power and trust as much as possible, in a way that's setting itself up to invest ever increasing amounts of confidence to keep the game going.

The alternative is to keep the scope of each organization narrow, overtly ask for trust for each venture separately, and make it clear what sorts of programs are being funded. For instance, Giving What We Can should go back to its initial focus of global poverty relief.

Like many EA leaders, I happen to believe that anything you can do to steer the far future in a better direction is much, much more consequential for the well-being of sentient creatures than any purely short-run improvement you can create now. So it might seem odd that I think Giving What We Can should stay focused on global poverty. But, I believe that the single most important thing we can do to improve the far future is hold onto our ability to accurately build shared models. If we use bait-and-switch tactics, we are actively eroding the most important type of capital we have – coordination capacity.

If you do not think giving 10% of one's income to global poverty charities is the right thing to do, then you can't in full integrity urge others to do it – so you should stop. You might still believe that GWWC ought to exist. You might still believe that it is a positive good to encourage people to give much of their income to help the global poor, if they wouldn't have been doing anything else especially effective with the money. If so, and you happen to find yourself in charge of an organization like Giving What We Can, the thing to do is write a letter to GWWC members telling them that you've changed your mind, and why, and offering to give away the brand to whoever seems best able to honestly maintain it.

If someone at the Centre for Effective Altruism fully believes in GWWC's original mission, then that might make the transition easier. If not, then one still has to tell the truth and do what's right.

And what of the EA Funds? The Long-Term Future Fund is run by Open Philanthropy Project Program Officer Nick Beckstead. If you think that it's a good thing to delegate giving decisions to Nick, then I would agree with you. Nick's a great guy! I'm always happy to see him when he shows up at house parties. He's smart, and he actively seeks out arguments against his current point of view. But the right thing to do, if you want to persuade people to delegate their giving decisions to Nick Beckstead, is to make a principled case for delegating giving decisions to Nick Beckstead. If the Centre for Effective Altruism did that, then Nick would almost certainly feel more free to allocate funds to the best things he knows about, not just the best things he suspects EA Funds donors would be able to understand and agree with.

If you can't directly persuade people, then maybe you're wrong. If the problem is inferential distance, then you've got some work to do bridging that gap.

There's nothing wrong with setting up a fund to make it easy. It's actually a really good idea. But there is something wrong with the multiple layers of vague indirection involved in the current marketing of the Far Future fund – using global poverty to sell the generic idea of doing the most good, then using CEA's identity as the organization in charge of doing the most good to persuade people to delegate their giving decisions to it, and then sending their money to some dude at the multi-billion-dollar foundation to give away at his personal discretion. The same argument applies to all four Funds.

Likewise, if you think that working directly on AI risk is the most important thing, then you should make arguments directly for working on AI risk. If you can't directly persuade people, then maybe you're wrong. If the problem is inferential distance, it might make sense to imitate the example of someone like Eliezer Yudkowsky, who used indirect methods to bridge the inferential gap by writing extensively on individual human rationality, and did not try to control others' actions in the meantime.

If Holden thinks he should be in charge of some AI safety research, then he should ask Good Ventures for funds to actually start an AI safety research organization. I'd be excited to see what he'd come up with if he had full control of and responsibility for such an organization. But I don't think anyone has a good plan to work directly on AI risk, and I don't have one either, which is why I'm not directly working on it or funding it. My plan for improving the far future is to build human coordination capacity.

(If, by contrast, Holden just thinks there needs to be coordination between different AI safety organizations, the obvious thing to do would be to work with FLI on that, e.g. by giving them enough money to throw their weight around as a funder. They organized the successful Puerto Rico conference, after all.)

Another thing that would be encouraging would be if at least one of the Funds were not administered entirely by an Open Philanthropy Project staffer, and ideally an expert who doesn't benefit from the halo of "being an EA." For instance, Chris Blattman is a development economist with experience designing programs that don't just use but generate evidence on what works. When people were arguing about whether sweatshops are good or bad for the global poor, he actually went and looked by performing a randomized controlled trial. He's leading two new initiatives with J-PAL and IPA, and expects that directors designing studies will also have to spend time fundraising. Having funding lined up seems like the sort of thing that would let them spend more time actually running programs. And more generally, he seems likely to know about funding opportunities the Open Philanthropy Project doesn't, simply because he's embedded in a slightly different part of the global health and development network.

Narrower projects that rely less on the EA brand and more on what they're actually doing, and more cooperation on equal terms with outsiders who seem to be doing something good already, would do a lot to help EA grow beyond putting stickers on its own behavior chart. I'd like to see EA grow up. I'd be excited to see what it might do.

Summary

  1. Good programs don't need to distort the story people tell about them, while bad programs do.
  2. Moral confidence games – treating past promises and trust as a track record to justify more trust – are an example of the kind of distortion mentioned in (1), that benefits bad programs more than good ones.
  3. The Open Philanthropy Project's Open AI grant represents a shift from evaluating other programs' effectiveness, to assuming its own effectiveness.
  4. EA Funds represents a shift from EA evaluating programs' effectiveness, to assuming EA's effectiveness.
  5. A shift from evaluating other programs' effectiveness, to assuming one's own effectiveness, is an example of the kind of "moral confidence game" mentioned in (2).
  6. EA ought to focus on scope-limited projects, so that it can directly make the case for those particular projects instead of relying on EA identity as a reason to support an EA organization.
  7. EA organizations ought to entrust more responsibility to outsiders who seem to be doing good things but don't overtly identify as EA, instead of trying to keep it all in the family.
(Cross-posted at my personal blog and the EA Forum.

Disclosure: I know many people involved at many of the organizations discussed, and I used to work for GiveWell. I have no current institutional affiliation to any of them. Everyone mentioned has always been nice to me and I have no personal complaints.)

April '17 I Care About Thread

4 MaryCh 18 April 2017 02:08PM

As an experiment, here's a thread for people to post about things they care about. Specifically, for things that are possible to contribute to, in some way, and preferably, to invite others to join.

Mine is buying and donating highschool textbooks to schools in the 'grey zone' of Ukraine (where the war kinda isn't fought, but few people would be surprised if it started.) I don't deliver them myself, though.

What's yours?

Straw Hufflepuffs and Lone Heroes

23 Raemon 16 April 2017 11:48PM
I was hoping the next Project Hufflepuff post would involve more "explain concretely what I think we should do", but as it turns out I'm still hashing out some thoughts about that. In the meanwhile, this is the post I actually have ready to go, which is as good as any to post for now.

Epistemic Status: Mythmaking. This is tailored for the sort of person for whom the "Lone Hero" mindset is attractive. If that isn't something you're concerned with and this post feels irrelevant or missing some important things, note that my vision for Project Hufflepuff has multiple facets and I expect different people to approach it in different ways.

The Berkeley Hufflepuff Unconference is on April 28th. RSVPing on this Facebook Event is helpful, as is filling out this form.



For good or for ill, the founding mythology of our community is a Harry Potter fanfiction.

This has a few ramifications I’ll delve into at some point, but the most pertinent bit is: for a community to change itself, the impulse to change needs to come from within the community. I think it’s easier to build change off of stories that are already a part of our cultural identity.*

* with an understanding that maybe part of the problem is that our cultural identity needs to change, or be more accessible, but I’m running with this mythos for the time being.

In J.K Rowling’s original Harry Potter story, Hufflepuffs are treated like “generic background characters” at best and as a joke at worst. All the main characters are Gryffindors, courageous and true. All the bad guys are Slytherin. And this is strange - Rowling clearly was setting out to create a complex world with nuanced virtues and vices. But it almost seems to me like Rowling’s story takes place in an alternate, explicitly “Pro-Gryffindor propaganda” universe instead of the “real” Harry Potter world. 

People have trouble taking Hufflepuff seriously, because they’ve never actually seen the real thing - only lame, strawman caricatures.

Harry Potter and the Methods of Rationality is… well, Pro-Ravenclaw propaganda. But part of being Ravenclaw is trying to understand things, and to use that knowledge. Eliezer makes an earnest effort to steelman each house. What wisdom does it offer that actually makes sense? What virtues does it cultivate that are rare and valuable?

When Harry goes under the sorting hat, it actually tries to convince him not to go into Ravenclaw, and specifically pushes towards Hufflepuff House:

Where would I go, if not Ravenclaw?

"Ahem. 'Clever kids in Ravenclaw, evil kids in Slytherin, wannabe heroes in Gryffindor, and everyone who does the actual work in Hufflepuff.' This indicates a certain amount of respect. You are well aware that Conscientiousness is just about as important as raw intelligence in determining life outcomes, you think you will be extremely loyal to your friends if you ever have some, you are not frightened by the expectation that your chosen scientific problems may take decades to solve -"

I'm lazy! I hate work! Hate hard work in all its forms! Clever shortcuts, that's all I'm about!

"And you would find loyalty and friendship in Hufflepuff, a camaraderie that you have never had before. You would find that you could rely on others, and that would heal something inside you that is broken."

But my plans -

"So replan! Don't let your life be steered by your reluctance to do a little extra thinking. You know that."

In the end, Harry chooses to go to Ravenclaw - the obvious house, the place that seemed most straightforward and comfortable. And ultimately… a hundred+ chapters later, I think he’s still visibly lacking in the strengths that Hufflepuff might have helped him develop. 

He does work hard and is incredibly loyal to his friends… but he operates in a fundamentally lone-wolf mindset. He’s still manipulating people for their own good. He’s still too caught up in his own cleverness. He never really has true friends other than Hermione, and when she is unable to be his friend for an extended period of time, it takes a huge toll on him that he doesn’t have the support network to recover from in a healthy way. 

The story does showcase Hufflepuff virtue. Hermione’s army is strong precisely because people work hard, trust each other and help each other - not just in big, dramatic gestures, but in small moments throughout the day. 

But… none of that ends up really mattering. And in the end, Harry faces his enemy alone. Lip service is paid to the concepts of friendship and group coordination, but the dominant narrative is Godric Gryffindor’s Nihil Supernum:


No rescuer hath the rescuer.
No lord hath the champion.
No mother or father.
Only nothingness above.


The Sequences and HPMOR both talk about the importance of groups, of emotions, of avoiding the biases that plague overly-clever people in particular. But I feel like the communities descended from Less Wrong, as a whole, are still basically that eleven-year-old Harry Potter: abstractly understanding that these things are important, but not really believing in them seriously enough to actually change their plans and priorities.

Lone Heroes


In Methods of Rationality, there’s a pretty good reason for Harry to focus on being a lone hero: he literally is alone. Nobody else really cares about the things he cares about or tries to do things on his level. It’s like a group project in high school, which is supposed to teach cooperation but actually just results in one kid doing all the work while the others either halfheartedly try to help (at best) or deliberately goof off.

Harry doesn’t bother turning to others for help, because they won’t give him the help he needs.

He does the only thing he can do reliably: focus on himself, pushing himself as hard as he can. The world is full of impossible challenges and nobody else is stepping up, so he shuts up and does the impossible as best he can. Learning higher level magic. Learning higher level strategy. Training, physically and mentally. 

This proves to be barely enough to survive, and not nearly enough to actually play the game. The last chapters are Harry realizing his best still isn’t good enough, and no, this isn’t fair, but it’s how the world is, and there’s nothing to do but keep trying.

He helps others level up as best they can. Hermione and Neville and some others show promise. But they’re not ready to work together as equals.

And frankly, this does match my experience of the real world. When you have a dream burning in your heart... it is incredibly hard to find someone who shares it, who will not just pitch in and help but will actually move heaven and earth to achieve it. 

And if they aren’t capable, level themselves up until they are.

In my own projects, I have tried to find people to work alongside me and at best I’ve found temporary allies. And it is frustrating. And it is incredibly tempting to say “well, the only person I can rely on is myself.”

But… here’s the thing.

Yes, the world is horribly unfair. It is full of poverty, and people trapped in demoralizing jobs. It is full of stupid bureaucracies and corruption and people dying for no good reason. It is full of beautiful things that could exist but don’t. And there are terribly few people who are able and willing to do the work needed to make a dent in reality.

But as long as we’re willing to look at monstrously unfair things and roll up our sleeves and get to work anyway, consider this:

It may be that one of the unfair things is that one person can never be enough to solve these problems. That one of the things we need to roll up our sleeves and do even though it seems impossible is figure out how to coordinate and level up together and rely on each other in a way that actually works.

And maybe, while we’re at it, find meaningful relationships that actually make us happy. Because it's not a coincidence that Hufflepuff is about both hard work and warmth and camaraderie. The warmth is what makes the hard work sustainable.

Godric Gryffindor has a point, but Nihil Supernum feels incomplete to me. There are no parents to step in and help us, but if we look to our left, or right…


Yes, you are only one
No, it is not enough—
But if you lift your eyes,
I am your brother

Vienna Teng, Level Up 


-


Reminder that the Berkeley Hufflepuff Unconference is on April 28th. RSVPing on this Facebook Event is helpful, as is filling out this form.


Computation Hazards

15 Alex_Altair 13 June 2012 09:49PM
This is a summary of material from various posts and discussions. My thanks to Eliezer Yudkowsky, Daniel Dewey, Paul Christiano, Nick Beckstead, and several others.

Several ideas have been floating around LessWrong that can be organized under one concept, relating to a subset of AI safety problems. I’d like to gather these ideas in one place so they can be discussed as a unified concept. To give a definition:

A computation hazard is a large negative consequence that may arise merely from vast amounts of computation, such as in a future supercomputer.

For example, suppose a computer program needs to model people very accurately to make some predictions, and it models those people so accurately that the "simulated" people can experience conscious suffering. In a very large computation of this type, millions of people could be created, suffer for some time, and then be destroyed when they are no longer needed for making the predictions desired by the program. This idea was first mentioned by Eliezer Yudkowsky in Nonperson Predicates.

There are other hazards that may arise in the course of running large-scale computations. In general, we might say that:

Large amounts of computation will likely consist in running many diverse algorithms. Many algorithms are computation hazards. Therefore, all else equal, the larger the computation, the more likely it is to produce a computation hazard.

Of course, most algorithms may be morally neutral. Furthermore, algorithms must be somewhat complex before they could possibly be a hazard. For instance, it is intuitively clear that no eight-bit program could possibly be a computation hazard on a normal computer. Worrying computations therefore fall into two categories: computations that run most algorithms, and computations that are particularly likely to run algorithms that are computation hazards.

An example of a computation that runs most algorithms is a mathematical formalism called Solomonoff induction. First published in 1964, it is an attempt to formalize the scientific process of induction using the theory of Turing machines. It is a brute-force method that finds hypotheses to explain data by testing all possible hypotheses. Many of these hypotheses may be algorithms that describe the functioning of people. At a sufficient precision, these algorithms themselves may experience consciousness and suffering. Taken literally, Solomonoff induction runs all algorithms; therefore it produces all possible computation hazards. If we are to avoid computation hazards, any implemented approximations of Solomonoff induction will need to determine ahead of time which algorithms are computation hazards.

Computations that run most algorithms could also hide in other places. Imagine a supercomputer’s power is being tested on a simple game, like chess or Go. The testing program simply tries all possible strategies, according to some enumeration. The best strategy that the supercomputer finds would be a measure of how many computations it could perform, compared to other computers that ran the same program. If the rules of the game are complex enough to be Turing complete (a surprisingly easy achievement) then this game-playing program would eventually simulate all algorithms, including ones with moral status.

Of course, running most algorithms is quite infeasible simply because of the vast number of possible algorithms. Depending on the fraction of algorithms that are computation hazards, it may be enough that a computation run an enormous number which act as a random sample of all algorithms. Computations of this type might include evolutionary programs, which are blind to the types of algorithms they run until the results are evaluated for fitness. Or they may be Monte Carlo approximations of massive computations.

But if computation hazards are relatively rare, then it will still be unlikely for large-scale computations to stumble across them unguided. Several computations may fall into the second category of computations that are particularly likely to run algorithms that are computation hazards. Here we focus on three types of computations in particular: agents, predictors and oracles. The last two types are especially important because they are often considered safer types of AI than agent-based AI architectures. First I will stipulate definitions for these three types of computations, and then I will discuss the types of computation hazards they may produce.

Agents

An agent is a computation which decides between possible actions based on the consequences of those actions. They can be thought of as “steering” the future towards some target, or as selecting a future from the set of possible futures. Therefore they can also be thought of as having a goal, or as maximizing a utility function.

Sufficiently powerful agents are extremely powerful because they constitute a feedback loop. Well-known from physics, feedback loops often change their surroundings incredibly quickly and dramatically. Examples include the growth of biological populations, and nuclear reactions. Feedback loops are dangerous if their target is undesirable. Agents will be feedback loops as soon as they are able to improve their ability to improve their ability to move towards their goal. For example, humans can improve their ability to move towards their goal by using their intelligence to make decisions. A student aiming to create cures can use her intelligence to learn chemistry, therefore improving her ability to decide what to study next. But presently, humans cannot improve their intelligence, which would improve their ability to improve their ability to make decisions. The student cannot yet learn how to modify her brain in order for her to more quickly learn subjects.

Predictors

A predictor is a computation which takes data as input, and predicts what data will come next. An example would be certain types of trained neural networks, or any approximation of Solomonoff induction. Intuitively, this feels safer than an agent AI because predictors do not seem to have goals or take actions; they just report predictions as requested by human.

Oracles

An oracle is a computation which takes questions as input, and returns answers. They are broader than predictors in that one could ask an oracle about predictions. Similar to a predictor, oracles do not seem to have goals or take actions. (Some material summarized here.)

Examples of hazards

Agent-like computations are the most clearly dangerous computation hazards. If any large computation starts running the beginning of a self-improving agent computation, it is difficult to say how far the agent may safely be run before it is a computation hazard. As soon as the agent is sufficiently intelligent, it will attempt to acquire more resources like computing substrate and energy. It may also attempt to free itself from control of the parent computation.

Another major concern is that, because people are an important part of the surroundings, even non-agent predictors or oracles will simulate people in order to make predictions or give answers respectively. Someone could ask a predictor, “What will this engineer do if we give him a contract?” It may be that the easiest way for the predictor to determine the answer is to simulate the internal workings of the given engineer's mind. If these simulations are sufficiently precise, then they will be people in and of themselves. The simulations could cause those people to suffer, and will likely kill them by ending the simulation when the prediction or answer is given.

Similarly, one can imagine that a predictor or oracle might simulate powerful agents; that is, algorithms which efficiently maximize some utility function. Agents may be simulated because many agent-like entities exist in the real world, and their behavior would need to be modeled. Or, perhaps oracles would investigate agents for the purpose of answering questions better. These agents, while being simulated, may have goals that require acting independently of the oracle. These agents may also be more powerful than the oracles, especially since the oracles were not designed with self-improvement behavior in mind. Therefore these agents may attempt to “unbox” themselves from the simulation and begin controlling the rest of the universe. For instance, the agents may use previous questions given to the oracle to deduce the nature of the universe and the psychology of the oracle-creators. (For a fictional example, see That Alien Message.) Or, the agent might somehow distort the output of the predictor, in a way that what the oracle predicts will cause us to unbox the agent.

Predictors also have the problem of self-fulfilling prophecies (first suggested here). An arbitrarily accurate predictor will know that its prediction will affect the future. Therefore, to be a correct prediction, it must make sure that delivering its prediction doesn’t cause the receiver to act in a way that negates the prediction. Therefore, the predictor may have to choose between predictions which cause the receiver to act in a way that fulfills the prediction. This is a type of control over the user. Since the predictor is super-intelligent, any control may rapidly optimize the universe towards some unknown goal.

Overall, there is a large worry that sufficiently intelligent oracles or predictors may become agents. Beside the above possibilities, some are worried that intelligence is inherently an optimization process, and therefore oracles and predictors are inherently satisfying some utility function. This, combined with the fact that nothing can be causally isolated from the rest of the universe, seems to invite an eventual AI-takeoff.

Methods for avoiding computational hazards

It is often thought that, while no proposal has yet been shown safe from computational hazards, oracles and predictors are safer than deliberately agent-based AGI. Other methods have been proposed to make these even safer. Armstrong et al. describe many AI safety measures in general. Below we review some possible techniques for avoiding computational hazards specifically.

One obvious safety practice is to limit the complexity, or the size of computations. In general, this will also limit the algorithm below general intelligence, but it is a good step while progressing towards FAI. Indeed, it is clear that all current prediction or AI systems are too simple to either be general intelligences, or pose as a computational hazard.

A proposal for regulating complex oracles or predictors is to develop safety indicators. That is, develop some function that will evaluate the proposed algorithm or model, and return whether it is potentially dangerous. For instance, one could write a simple program that rejects running an algorithm if any part of it is isomorphic to the human genome (since DNA clearly creates general intelligence and people under the right circumstances). Or, to measure the impact of an action suggested by an oracle, one could ask how many humans would be alive one year after the action was taken.

But one could only run an algorithm if they were sure it was not a person. A function that could evaluate an algorithm and return 0 only if it is not a person is called a nonperson predicate. Some algorithms are obviously not people. For example, squaring the numbers from 1 to 100 will not simulate people. Any algorithm whose behavior is periodic with a short period is unlikely to be a person, or nearly any presently constructed software. But in general this seems extremely difficult to verify. It could be that writing nonperson predicates or other safety indicators is FAI-complete in that sense that if we solve them, we will have discovered friendliness theory. Furthermore, it may be that some attempts to evaluate whether an algorithm is a person actually causes a simulation of a person, by running parts of the algorithm, by modeling a person for comparison, or by other means. Similarly, it may be that attempts to investigate the friendliness of a particular agent cause that agent to unbox itself.

Predictors seem to be one of the most goal-agnostic forms of AGI. This makes them a very attractive model in which to perfect safety. Some ideas for avoiding self-fulfilling predictions suggest that we ask the predictor to tell us what it would have predicted if we hadn’t asked (first suggested here). This frees the predictor from requiring itself to make predictions consistent with our behavior. Whether this will work depends on the exact process of the predictor; it may be so accurate that it cannot deal with counterfactuals, and will simply report that it would have predicted that we would have asked anyway. It is also problematic that the prediction is now inaccurate; because it has told us, we will act, possibly voiding any part of the prediction.

A very plausible but non-formal solution is to aim for a soft takeoff. For example, we could build a predictor that is not generally intelligent, and use it to investigate safe ways advance the situation. Perhaps we could use a sub-general intelligence to safely improve our own intelligence.

Have I missed any major examples in this post? Does “computation hazards” seem like a valid concept as distinct from other types of AI-risks?

References

Armstrong S., Sandberg A., Bostrom N. (2012). “Thinking inside the box: using and controlling an Oracle AI”. Minds and Machines, forthcoming.

Solomonoff, R., "A Formal Theory of Inductive Inference, Part I" Information and Control, Vol 7, No. 1 pp 1-22, March 1964.

Solomonoff, R., "A Formal Theory of Inductive Inference, Part II" Information and Control, Vol 7, No. 2 pp 224-254, June 1964.

ALBA: can you be "aligned" at increased "capacity"?

3 Stuart_Armstrong 13 April 2017 07:23PM

Crossposted at the Intelligent Agents Forum.

I think that Paul Christiano's ALBA proposal is good in practice, but has conceptual problems in principle.

Specifically, I don't think it makes sense to talk about bootstrapping an "aligned" agent to one that is still "aligned" but that has an increased capacity.

The main reason being that I don't see "aligned" as being a definition that makes sense distinct from capacity.

 

These are not the lands of your forefathers

Here's a simple example: let r be a reward function that is perfectly aligned with human happiness within ordinary circumstances (and within a few un-ordinary circumstances that humans can think up).

Then the initial agent - B0, a human - trains a reward r1 for an agent A1. This agent is limited in some way - maybe it doesn't have much speed or time - but the aim is for r1 to ensure that A1 is aligned with B0.

Then the capacity of A1 is increased to B1, a slow powerful agent. It computers the reward r2 to ensure the alignment of A2, and so on.

The nature of the Bj agents is not defined - they might be algorithms calling Ai for i ≤ j as subroutines, humans may be involved, and so on.

If the humans are unimaginative and don't deliberately seek out more extreme and exotic test cases, the best case scenario is for ri → r as i → ∞.

And eventually there will be an agent An that is powerful enough to overwhelm the whole system and take over. It will do this in full agreement with Bn-1, because they share the same objective. And then An will push the world into extra-ordinary circumstance and proceed to maximise r, with likely disastrous results for us humans.

 

The nature of the problem

So what went wrong? At what point did the agents go out of alignment?

In one sense, at An. In another sense, at A1 (and, in another interesting sense, at B0, the human). The reward r was aligned, as long as the agent stayed near the bounds of the ordinary. As soon as it was no longer restricted to that, it went out of alignment, not because of a goal drift, but because of a capacity increase.

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