Emerging technologies such as artificial intelligence could radically change the trajectory of our civilization. We are building a global community of researchers and professionals working to ensure that this technological transformation does not risk causing suffering on an unprecedented scale.
We do research, award grants and scholarships, and host workshops. Our work focuses on advancing the safety and governance of artificial intelligence as well as understanding other long-term risks.
The quest for artificial intelligence poses questions relating to decision theory: How can we implement any given decision theory in an AI? Which decision theory (if any) describes the behavior of any existing AI design? This paper examines which decision theory (in particular, evidential or causal) is implemented by an approval-directed agent, i.e., an agent whose goal it is to maximize the score it receives from an overseer.
Download
Read online
One approach to achieving cooperation in the one-shot prisoner’s dilemma is Tennenholtz’s program equilibrium, in which the players of a game submit programs instead of strategies. These programs are then allowed to read each other’s source code to decide which action to take. Unfortunately, existing cooperative equilibria are either fragile or computationally challenging and therefore unlikely to be realized in practice. This paper proposes a new, simple, more efficient program to achieve more robust cooperative program equilibria.
Download
Read online
Discussions about the possible consequences of creating superintelligence have included the possibility of existential risk, usually understood as the risk of human extinction. We argue that suffering risks (s-risks) present comparable severity and probability. Just as with existential risks, s-risks can be caused as well as reduced by superintelligent AI.
Download
Read online
This post discusses cause prioritization from the perspective of downside-focused value systems, i.e. views whose primary concern is the reduction of bads such as suffering. According to such value systems, interventions which reduce risks of astronomical suffering are likely more promising than interventions which primarily reduce extinction risks.
Read online
Summary Dictators who exhibited highly narcissistic, psychopathic, or sadistic traits were involved in some of the greatest catastrophes in human history. Malevolent individuals in positions of power could negatively affect humanity’s long-term trajectory by, for example, exacerbating international conflict or other broad risk factors. Malevolent humans with access to advanced technology—such as whole brain emulation […]
Read moreTraditional disaster risk prevention has a concept of risk factors. These factors are not risks in and of themselves, but they increase either the probability or the magnitude of a risk. For instance, inadequate governance structures do not cause a specific disaster, but if a disaster strikes it may impede an effective response, thus increasing the damage. Rather than considering individual scenarios of how s-risks could occur, which tends to be highly speculative, this post instead looks at risk factors – i.e. factors that would make s-risks more likely or more severe.
Read moreSurrogate goals might be one of the most promising approaches to reduce (the disvalue resulting from) threats. The idea is to add to one’s current goals a surrogate goal that one did not initially care about, hoping that any potential threats will target this surrogate goal rather than what one initially cared about. In this post, I will outline two key obstacles to a successful implementation of surrogate goals.
Read more
Suppose that an altruistic and morally motivated agent who is uncertain between evidential decision theory (EDT) and causal decision theory (CDT) finds herself in a situation in which the two theories give conflicting verdicts. We argue that even if she has significantly higher credence in CDT, she should nevertheless act in accordance with EDT.
Download
Read online
The quest for artificial intelligence poses questions relating to decision theory: How can we implement any given decision theory in an AI? Which decision theory (if any) describes the behavior of any existing AI design? This paper examines which decision theory (in particular, evidential or causal) is implemented by an approval-directed agent, i.e., an agent whose goal it is to maximize the score it receives from an overseer.
Download
Read online
