  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.
