Artificial intelligence is advancing quickly. In some ways, AI development is an uncharted frontier, but in others, it follows the familiar pattern of other competitive processes; these include biological evolution, cultural change, and competition between businesses. In each of these, there is significant variation between individuals structures and some are copied more than others, with the result that the future population is more similar to the most copied individuals of the earlier generation. In this way, species evolve, cultural ideas are transmitted across generations, and successful businesses are imitated while unsuccessful ones disappear.
This paper argues that these same selection patterns will shape AI development and that the features that will be copied the most are likely to create an AI population that is dangerous to humans. As AIs become faster and more reliable than people at more and more tasks, businesses that allow AIs to perform more of their work will outperform competitors still using human labor at any stage, just as a modern clothing company that insisted on using only manual looms would be easily outcompeted by those that use industrial looms. Companies will need to increase their reliance on AIs to stay competitive, and the companies that use AIs best will dominate the marketplace. This trend means that the AIs most likely to be copied will be very efficient at achieving their goals autonomously with little human intervention.
A world dominated by increasingly powerful, independent, and goal-oriented AIs is dangerous. Today, the most successful AI models are not transparent, and even their creators do not fully know how they work or what they will be able to do before they do it. We know only their results, not how they arrived at them. As people give AIs the ability to act in the real world, the AIs’ internal processes will still be inscrutable: we will be able to measure their performance only based on whether or not they are achieving their goals. This means that the AIs humans will see as most successful — and therefore the ones that are copied — will be whichever AIs are most effective at achieving their goals, even if they use harmful or illegal methods, as long as we do not detect their bad behavior.
In natural selection, the same pattern emerges: individuals are cooperative or even altruistic in some situations, but ultimately, strategically selfish individuals are best able to propagate. A business that knows how to steal trade secrets or deceive regulators without getting caught will have an edge over one that refuses to ever engage in fraud on principle. During a harsh winter, an animal that steals food from others to feed its own children will likely have more surviving offspring. Similarly, the AIs that succeed most will be those able to deceive humans, seek power, and achieve their goals by any means necessary.
If AI systems are more capable than we are in many domains and tend to work toward their goals even if it means violating our wishes, will we be able to stop them? As we become increasingly dependent on AIs, we may not be able to stop AI’s evolution. Humanity has never before faced a threat that is as intelligent as we are or that has goals. Unless we take thoughtful care, we could find ourselves in the position faced by wild animals today: most humans have no particular desire to harm gorillas, but the process of harnessing our intelligence toward our own goals means that they are at risk of extinction, because their needs conflict with human goals.
This paper proposes several steps we can take to combat selection pressure and avoid that outcome. We are optimistic that if we are careful and prudent, we can ensure that AI systems are beneficial for humanity. But if we do not extinguish competition pressures, we risk creating a world populated by highly intelligent lifeforms that are indifferent or actively hostile to us. We do not want the world that is likely to emerge if we allow natural selection to determine how AIs develop. Now, before AIs are a significant danger, is the time to begin ensuring that they develop safely.
Note: This paper articulates a concern that emerges in multi-agent situations. Of particular interest to this community might be Section 3.4 (existing moral systems may not be human compatible (e.g., impartial consequentialism)) and Section 4.3.2 (Leviathan).
I think https://www.alignmentforum.org/posts/TATWqHvxKEpL34yKz/intelligence-or-evolution is somewhat related in case you haven't seen it.
In §3.1–3.3, you look at the main known ways that altruism between humans has evolved — direct and indirect reciprocity, as well as kin and group selection — and ask whether we expect such altruism from AI towards humans to be similarly adaptive.
However, as observed in R. Joyce (2007). The Evolution of Morality (p. 5),
This is a subtle distinction which demands careful inspection.
In particular, are there circumstances under which AI training procedures and/or market or evolutionary incentives may produce psychological mechanisms which lead to altruistic behavior towards human beings, even when that altruistic behavior is not adaptive? For example, could altruism learned towards human beings early on, when humans have something to offer in return, be “sticky” later on (perhaps via durable, self-perpetuating power structures), when humans have nothing useful to offer? Or could learned altruism towards other AIs be broadly-scoped enough that it applies to humans as well, just as human altruistic tendencies sometimes apply to animal species which can offer us no plausible reciprocal gain? This latter case is analogous to the situation analyzed in your paper, and yet somehow a different result has (sometimes) occurred in reality than that predicted by your analysis.
I don’t claim the conclusion is wrong, but I think a closer look at this subtlety would give the arguments for it more force.
Although you don’t look at network reciprocity / spatial selection.
Even factory farming, which might seem like a counterexample, is not really. For the very existence of humans altruistically motivated to eliminate it — and who have a real shot at success — demands explanation under your analysis.