Preface to CLR's Research Agenda on Cooperation, Conflict, and TAI

I find myself someone confused by s-risks as defined here; it's easy to generate clearly typical cases that very few would want, and hard to figure out where the boundaries are, and thus hard to figure out how much I should imagine this motivation impacting the research.

That is, consider the "1950s sci-fi prediction," where a slightly-more-competent version of humanity manages to colonize lots of different planets in ways that make them sort of duplicates of Earth. This seems like it would count as an s-risk if each planet has comparable levels of suffering to modern Earth and there are vastly more such planets. While this feels to me like "much worse than is possible," I'm not yet sold it's below the "ok" bar in the maxipok sense, but also it wouldn't seem too outlandish to think it's below that bar (depending on how bad you think life on Earth is now).

Do you think focusing on s-risks leads to meaningfully different technical goals than focusing on other considerations? I don't get that sense from the six headings, but I can imagine how it might add different constraints or different focus for some of them. For example, on the point of AI strategy and governance, it seems easiest to encourage cooperation when there are no external forces potentially removing participants from a coalition, but adding in particular ethical views possibly excludes people who could have been included. You might imagine, say, a carnivorous TAI developer who wants factory farming to make it to the stars.

This isn't necessarily a point against this view, according to me; it definitely is the case that focusing on alignment at all implies having some sort of ethical view or goal you want to implement, and it may be the case that being upfront about those goals simplifies or directs the technical work, as opposed to saying "we'll let future-us figure out what the moral goals are, first let's figure out how to implement any goals at all." But it does make me interested in how much disagreement you think there is on the desirability of future outcomes, weighted by their likelihood or something, between people primarily motivated by continued existence of human civilization and people primarily motivated by avoiding filling the universe with suffering or whatever other categories you think are worth considering.

Flo's summary for the Alignment Newsletter:

This agenda by the Effective Altruism Foundation focuses on risks of astronomical suffering (s-risks) posed by <@transformative AI@ >(@Defining and Unpacking Transformative AI@) (TAI) and especially those related to conflicts between powerful AI agents. This is because there is a very clear path from extortion and executed threats against altruistic values to s-risks. While especially important in the context of s-risks, cooperation between AI systems is also relevant from a range of different viewpoints. The agenda covers four clusters of topics: strategy, credibility and bargaining, current AI frameworks, as well as decision theory.

The extent of cooperation failures is likely influenced by how power is distributed after the transition to TAI. At first glance, it seems like widely distributed scenarios (as <@CAIS@>(@Reframing Superintelligence: Comprehensive AI Services as General Intelligence@)) are more problematic, but related literature from international relations paints a more complicated picture. The agenda seeks a better understanding of how the distribution of power affects catastrophic risk, as well as potential levers to influence this distribution. Other topics in the strategy/governance cluster include the identification and analysis of realistic scenarios for misalignment, as well as case studies on cooperation failures in humans and how they can be affected by policy.

TAI might enable unprecedented credibility, for example by being very transparent, which is crucial for both contracts and threats. The agenda aims at better models of the effects of credibility on cooperation failures. One approach to this is open-source game theory, where agents can see other agents' source codes. Promising approaches to prevent catastrophic cooperation failures include the identification of peaceful bargaining mechanisms, as well as surrogate goals. The idea of surrogate goals is for an agent to commit to act as if it had a different goal, whenever it is threatened, in order to protect its actual goal from threats.

As some aspects of contemporary AI architectures might still be present in TAI, it can be useful to study cooperation failure in current systems. One concrete approach to enabling cooperation in social dilemmas that could be tested with contemporary systems is based on bargaining over policies combined with punishments for deviations. Relatedly, it is worth investigating whether or not multi-agent training leads to human-like bargaining by default. This has implications on the suitability of behavioural vs classical game theory to study TAI. The behavioural game theory of human-machine interactions might also be important, especially in human-in-the-loop scenarios of TAI.

The last cluster discusses the implications of bounded computation on decision theory as well as the decision theories (implicitly) used by current agent architectures. Another focus lies on acausal reasoning and in particular the possibility of acausal trade, where different correlated AI's cooperate without any causal links between them.

Flo's opinion:

I am broadly sympathetic to the focus on preventing the worst outcomes and it seems plausible that extortion could play an important role in these, even though I worry more about distributional shift plus incorrigibility. Still, I am excited about the focus on cooperation, as this seems robustly useful for a wide range of scenarios and most value systems.

My opinion:

Under a suffering-focused ethics under which s-risks far overwhelm x-risks, I think it makes sense to focus on this agenda. There don't seem to be many plausible paths to s-risks: by default, we shouldn't expect them, because it would be quite surprising for an amoral AI system to think it was particularly useful or good for humans to _suffer_, as opposed to not exist at all, and there doesn't seem to be much reason to expect an immoral AI system. Conflict and the possibility of carrying out threats are the most plausible ways by which I could see this happening, and the agenda here focuses on neglected problems in this space.

However, under other ethical systems (under which s-risks are worse than x-risks, but do not completely dwarf x-risks), I expect other safety research to be more impactful, because the failure mode of an amoral AI system that doesn't care about you seems both more likely and more amenable to technical safety approaches (to me at least).

To what degree is this sequence still relevant/up to date?

I translate AI-safety-related texts into Russian and now I'm chosing what to translate next.