The ethics of autonomous vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. Starting from the assumption that human harm is unavoidable, many authors have developed differing accounts of what morality requires in these situations. In this article, a strategy for AV decision-making is proposed, the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation: different road users hold different moral claims on the vehicle’s behavior, and the vehicle must mitigate these claims as it makes decisions about its environment. Using the context of autonomous vehicles, the harm produced by an action and the uncertainties connected to it are quantified and accounted for through deliberation, resulting in an ethical implementation coherent with reality. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of ‘moral positions’ concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an autonomous vehicle’s ethical decision making.

We address ethical dilemma situations that may arise during autonomous driving. To evaluate how this deliberation could work, we propose a decision-making algorithm based on a Markov Decision Process (MDP) which controls the vehicle in normal conditions. When a dilemma situation is detected, the collision severity is determined by an evaluation of the harm incurred by different types of road users. Then, to illustrate different moral approaches, three different policies are proposed: one based on ralwsian contractarianism, another on utilitarianism, and finally on egalitarianism. Each policy supports a different view of the concept of fairness, potentially producing different behaviors for the same dilemma situation.