Abstract
The doctrine of double effect (DDE) is a longstudied ethical principle that governs when actions
that have both positive and negative effects are to
be allowed. The goal in this paper is to automate
DDE. We briefly present DDE, and use a firstorder modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine.
We present formalizations of increasingly stronger
versions of the principle, including what is known
as the doctrine of triple effect. We then use our
framework to successfully simulate scenarios that
have been used to test for the presence of the principle in human subjects. Our framework can be
used in two different modes: One can use it to
build DDE-compliant autonomous systems from
scratch; or one can use it to verify that a given AI
system is DDE-compliant, by applying a DDE
layer on an existing system or model. For the latter
mode, the underlying AI system can be built using
any architecture (planners, deep neural networks,
bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes
a few parameters in its model, such verification is
possible. The role of the DDE layer here is akin
to a (dynamic or static) software verifier that examines existing software modules. Finally, we end
by sketching initial work on how one can apply our
DDE layer to the STRIPS-style planning model,
and to a modified POMDP model. This is preliminary work to illustrate the feasibility of the second
mode, and we hope that our initial sketches can be
useful for other researchers in incorporating DDE
in their own frameworks.