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There is much debate in machine ethics about the most appropriate way tointroduce ethical reasoning capabilities into intelligent autonomous machines.Recent incidents involving autonomous vehicles in which humans have been killedor injured have raised questions about how we ensure that such vehicles have anethical dimension to their behaviour and are therefore trustworthy. The mainproblem is that hardwiring such machines with rules not to cause harm or damageis not consistent with the notion of autonomy and intelligence. Also, suchethical hardwiring does not leave intelligent autonomous machines with anycourse of action if they encounter situations or dilemmas for which they arenot programmed or where some harm is caused no matter what course of action istaken. Teaching machines so that they learn ethics may also be problematicgiven recent findings in machine learning that machines pick up the prejudicesand biases embedded in their learning algorithms or data. This paper describesa fuzzy reasoning approach to machine ethics. The paper shows how it ispossible for an ethics architecture to reason when taking over from a humandriver is morally justified. The design behind such an ethical reasoner is alsoapplied to an ethical dilemma resolution case. One major advantage of theapproach is that the ethical reasoner can generate its own data for learningmoral rules (hence, autometric) and thereby reduce the possibility of pickingup human biases and prejudices. The results show that a new type ofmetric-based ethics appropriate for autonomous intelligent machines is feasibleand that our current concept of ethical reasoning being largely qualitative innature may need revising if want to construct future autonomous machines thathave an ethical dimension to their reasoning so that they become moralmachines.

When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?