  In order to construct an ethical artificial intelligence (AI) two complex
problems must be overcome. Firstly, humans do not consistently agree on what is
or is not ethical. Second, contemporary AI and machine learning methods tend to
be blunt instruments which either search for solutions within the bounds of
predefined rules, or mimic behaviour. An ethical AI must be capable of
inferring unspoken rules, interpreting nuance and context, possess and be able
to infer intent, and explain not just its actions but its intent. Using
enactivism, semiotics, perceptual symbol systems and symbol emergence, we
specify an agent that learns not just arbitrary relations between signs but
their meaning in terms of the perceptual states of its sensorimotor system.
Subsequently it can learn what is meant by a sentence and infer the intent of
others in terms of its own experiences. It has malleable intent because the
meaning of symbols changes as it learns, and its intent is represented
symbolically as a goal. As such it may learn a concept of what is most likely
to be considered ethical by the majority within a population of humans, which
may then be used as a goal. The meaning of abstract symbols is expressed using
perceptual symbols of raw sensorimotor stimuli as the weakest (consistent with
Ockham's Razor) necessary and sufficient concept, an intensional definition
learned from an ostensive definition, from which the extensional definition or
category of all ethical decisions may be obtained. Because these abstract
symbols are the same for both situation and response, the same symbol is used
when either performing or observing an action. This is akin to mirror neurons
in the human brain. Mirror symbols may allow the agent to empathise, because
its own experiences are associated with the symbol, which is also associated
with the observation of another agent experiencing something that symbol
represents.
