  This paper argues that training AI systems with absolute constraints -- which
forbid certain acts irrespective of the amount of value they might produce --
may make considerable progress on many AI safety problems in principle. First,
it provides a guardrail for avoiding the very worst outcomes of misalignment.
Second, it could prevent AIs from causing catastrophes for the sake of very
valuable consequences, such as replacing humans with a much larger number of
beings living at a higher welfare level. Third, it makes systems more
corrigible, allowing creators to make corrective interventions in them, such as
altering their objective functions or shutting them down. And fourth, it helps
systems explore their environment more safely by prohibiting them from
exploring especially dangerous acts. I offer a decision-theoretic formalization
of an absolute constraints, improving on existing models in the literature, and
use this model to prove some results about the training and behavior of
absolutist AIs. I conclude by showing that, although absolutist AIs will not
maximize expected value, they will not be susceptible to behave irrationally,
and they will not (contra coherence arguments) face environmental pressure to
become expected-value maximizers.
