Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety
This work addresses the problem of clarifying controversial safety strategies for future transformative AI systems, but it is incremental as it builds on existing discussions without introducing new methods or data.
The paper tackles the debate over Highly Reliable Agent Designs (HRAD) as an approach to AI safety by collecting and analyzing four central arguments used to justify it, aiming to reduce confusion in the field.
Several different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different and debated claims about the importance or usefulness of their work in the near term, and for future systems. Highly Reliable Agent Designs (HRAD) is one of the most controversial and ambitious approaches, championed by the Machine Intelligence Research Institute, among others, and various arguments have been made about whether and how it reduces risks from future AI systems. In order to reduce confusion in the debate about AI safety, here we build on a previous discussion by Rice which collects and presents four central arguments which are used to justify HRAD as a path towards safety of AI systems. We have titled the arguments (1) incidental utility,(2) deconfusion, (3) precise specification, and (4) prediction. Each of these makes different, partly conflicting claims about how future AI systems can be risky. We have explained the assumptions and claims based on a review of published and informal literature, along with consultation with experts who have stated positions on the topic. Finally, we have briefly outlined arguments against each approach and against the agenda overall.