  There is much debate in machine ethics about the most appropriate way to
introduce ethical reasoning capabilities into intelligent autonomous machines.
Recent incidents involving autonomous vehicles in which humans have been killed
or injured have raised questions about how we ensure that such vehicles have an
ethical dimension to their behaviour and are therefore trustworthy. The main
problem is that hardwiring such machines with rules not to cause harm or damage
is not consistent with the notion of autonomy and intelligence. Also, such
ethical hardwiring does not leave intelligent autonomous machines with any
course of action if they encounter situations or dilemmas for which they are
not programmed or where some harm is caused no matter what course of action is
taken. Teaching machines so that they learn ethics may also be problematic
given recent findings in machine learning that machines pick up the prejudices
and biases embedded in their learning algorithms or data. This paper describes
a fuzzy reasoning approach to machine ethics. The paper shows how it is
possible for an ethics architecture to reason when taking over from a human
driver is morally justified. The design behind such an ethical reasoner is also
applied to an ethical dilemma resolution case. One major advantage of the
approach is that the ethical reasoner can generate its own data for learning
moral rules (hence, autometric) and thereby reduce the possibility of picking
up human biases and prejudices. The results show that a new type of
metric-based ethics appropriate for autonomous intelligent machines is feasible
and that our current concept of ethical reasoning being largely qualitative in
nature may need revising if want to construct future autonomous machines that
have an ethical dimension to their reasoning so that they become moral
machines.
