  We present what we call the Interpretation Problem, whereby any rule in
symbolic form is open to infinite interpretation in ways that we might
disapprove of and argue that any attempt to build morality into machines is
subject to it. We show how the Interpretation Problem in Artificial
Intelligence is an illustration of Wittgenstein's general claim that no rule
can contain the criteria for its own application, and that the risks created by
this problem escalate in proportion to the degree to which to machine is
causally connected to the world, in what we call the Law of Interpretative
Exposure. Using game theory, we attempt to define the structure of normative
spaces and argue that any rule-following within a normative space is guided by
values that are external to that space and which cannot themselves be
represented as rules. In light of this, we categorise the types of mistakes an
artificial moral agent could make into Mistakes of Intention and Instrumental
Mistakes, and we propose ways of building morality into machines by getting
them to interpret the rules we give in accordance with these external values,
through explicit moral reasoning, the Show, not Tell paradigm, the adjustment
of causal power and structure of the agent, and relational values, with the
ultimate aim that the machine develop a virtuous character and that the impact
of the Interpretation Problem is minimised.
