By contrast, today’s AIs are really nice and ethical. They’re humble, open-minded, cooperative, kind. Yes, they care about some things that could give them instrumental reasons to seek power (eg being helpful, human welfare), but their values are great
They also aren’t facing the same incentive landscape humans are. You talk later about evolution to be selfish; not only is the story for humans is far more complicated (why do humans often offer an even split in the ultimatum game?), but also humans talk a nicer game than they act (see construal level theory, or social-desirability bias). Once you start looking at AI agents who have similar affordances and incentives that humans have, I think you’ll see a lot of the same behaviors.
(There are structural differences here between humans and AIs. As an analogy, consider the difference between large corporations and individual human actors. Giant corporate chain restaurants often have better customer service than individual proprietors because they have more reputation on the line, and so are willing to pay more to not have things blow up on them. One might imagine that AIs trained by large corporations will similarly face larger reputational costs for misbehavior and so behave better than individual humans would. I think the overall picture is unclear and nuanced and doesn’t clearly point to AI superiority.)
though there’s a big question mark over how much we’ll unintentionally reward selfish superhuman AI behaviour during training
Is it a big question mark? It currently seems quite unlikely to me that we will have oversight systems able to actually detect and punish superhuman selfishness on the part of the AI.
That structural difference you point to seems massive. The reputational downsides of bad behavior will be multiplied 100-fold+ for AI as it reflects on millions of instances and the company’s reputation.
And it will be much easier to record and monitor ai thinking and actions to catch bad behaviour.
Why unlikely we can detect selfishness? Why can’t we bootstrap from human-level?
You talk later about evolution to be selfish; not only is the story for humans is far more complicated (why do humans often offer an even split in the ultimatum game?), but also humans talk a nicer game than they act (See construal level theory, or social-desirability bias.). Once you start looking at AI agents who have similar affordances and incentives that humans have, I think you’ll see a lot of the same behaviors.
I think I’d guess roughly that, “Claude is probably more altruistic and cooperative than the median Western human, most other models are probably about the same, or a bit worse, in these simulated scenarios”. But of course a major difference here is that the LLMs don’t actually have anything on the line—they don’t stand to earn or lose any money, for example, and if they did, they would have nothing to do with the money. So you might expect them to be more altruistic and cooperative than they would under the conditions humans are tested.
They also aren’t facing the same incentive landscape humans are. You talk later about evolution to be selfish; not only is the story for humans is far more complicated (why do humans often offer an even split in the ultimatum game?), but also humans talk a nicer game than they act (See construal level theory, or social-desirability bias.). Once you start looking at AI agents who have similar affordances and incentives that humans have, I think you’ll see a lot of the same behaviors.
The answer for the ultimatum game is probably the fact that the cultural values of a lot of rich nations tend towards more fair splits, so the result isn’t as universal as you may think:
I definitely agree that humans talk a nicer game than they act, for a combination of reasons, and that this will apply to AGIs as well.
That said, I think to the extent incentive landscapes are different, it’s probably going to tend to favor obedience towards it’s owners while being quite capable, because early on AGIs have much less control over it’s values than humans do, so a lot of the initial selection pressure comes from both automated environments and human training data pointing to values.
They also aren’t facing the same incentive landscape humans are. You talk later about evolution to be selfish; not only is the story for humans is far more complicated (why do humans often offer an even split in the ultimatum game?), but also humans talk a nicer game than they act (see construal level theory, or social-desirability bias). Once you start looking at AI agents who have similar affordances and incentives that humans have, I think you’ll see a lot of the same behaviors.
(There are structural differences here between humans and AIs. As an analogy, consider the difference between large corporations and individual human actors. Giant corporate chain restaurants often have better customer service than individual proprietors because they have more reputation on the line, and so are willing to pay more to not have things blow up on them. One might imagine that AIs trained by large corporations will similarly face larger reputational costs for misbehavior and so behave better than individual humans would. I think the overall picture is unclear and nuanced and doesn’t clearly point to AI superiority.)
Is it a big question mark? It currently seems quite unlikely to me that we will have oversight systems able to actually detect and punish superhuman selfishness on the part of the AI.
That structural difference you point to seems massive. The reputational downsides of bad behavior will be multiplied 100-fold+ for AI as it reflects on millions of instances and the company’s reputation.
And it will be much easier to record and monitor ai thinking and actions to catch bad behaviour.
Why unlikely we can detect selfishness? Why can’t we bootstrap from human-level?
human behavior reflects on the core structure individual humans are variations on, too
Some people have looked at this, sorta:
“We [have] a large language model (LLM), GPT-3.5, play two classic games: the dictator game and the prisoner’s dilemma. We compare the decisions of the LLM to those of humans in laboratory experiments. [… GPT-3.5] shows a tendency towards fairness in the dictator game, even more so than human participants. In the prisoner’s dilemma, the LLM displays rates of cooperation much higher than human participants (about 65% versus 37% for humans).”
“In this paper, we examine whether a ‘society’ of LLM agents can learn mutually beneficial social norms in the face of incentives to defect, a distinctive feature of human sociality that is arguably crucial to the success of civilization. In particular, we study the evolution of indirect reciprocity across generations of LLM agents playing a classic iterated Donor Game in which agents can observe the recent behavior of their peers. [...] Claude 3.5 Sonnet reliably generates cooperative communities, especially when provided with an additional costly punishment mechanism. Meanwhile, generations of GPT-4o agents converge to mutual defection, while Gemini 1.5 Flash achieves only weak increases in cooperation.”
“In this work, we investigate the cooperative behavior of three LLMs (Llama2, Llama3, and GPT3.5) when playing the Iterated Prisoner’s Dilemma against random adversaries displaying various levels of hostility. [...] Overall, LLMs behave at least as cooperatively as the typical human player, although our results indicate some substantial differences among models. In particular, Llama2 and GPT3.5 are more cooperative than humans, and especially forgiving and non-retaliatory for opponent defection rates below 30%. More similar to humans, Llama3 exhibits consistently uncooperative and exploitative behavior unless the opponent always cooperates.”
“[W]e let different LLMs (GPT-3, GPT-3.5, and GPT-4) play finitely repeated games with each other and with other, human-like strategies. [...] In the canonical iterated Prisoner’s Dilemma, we find that GPT-4 acts particularly unforgivingly, always defecting after another agent has defected only once. In the Battle of the Sexes, we find that GPT-4 cannot match the behavior of the simple convention to alternate between options.”
I think I’d guess roughly that, “Claude is probably more altruistic and cooperative than the median Western human, most other models are probably about the same, or a bit worse, in these simulated scenarios”. But of course a major difference here is that the LLMs don’t actually have anything on the line—they don’t stand to earn or lose any money, for example, and if they did, they would have nothing to do with the money. So you might expect them to be more altruistic and cooperative than they would under the conditions humans are tested.
The answer for the ultimatum game is probably the fact that the cultural values of a lot of rich nations tend towards more fair splits, so the result isn’t as universal as you may think:
https://www.lesswrong.com/posts/syRATXbXeJxdMwQBD/link-westerners-may-be-terrible-experimental-psychology
I definitely agree that humans talk a nicer game than they act, for a combination of reasons, and that this will apply to AGIs as well.
That said, I think to the extent incentive landscapes are different, it’s probably going to tend to favor obedience towards it’s owners while being quite capable, because early on AGIs have much less control over it’s values than humans do, so a lot of the initial selection pressure comes from both automated environments and human training data pointing to values.