According to Joshua Greene’s dual-process theory, our moral judgments are processed in one of two systems in the brain referred to as the emotional (quick, unconscious) and rational (slow, conscious) system. The reason for why people tend to answer differently in the footbridge dilemma compared to the trolley dilemma is because the emotional system is dominating over the rational system. Research has demonstrated that patients with ventromedial prefrontal cortex damage make more utilitarian judgments in moral dilemmas in relation to harmful situations. According to the dual-process theory, this is because the emotional system has been impaired which results in that the only working system is the rational system. The aim of this thesis is to investigate how the dual-process theory tries to explain why our moral judgments tend to differ in some moral dilemmas. This thesis will also look at how the dual-process theory tries to explain why patients with ventromedial prefrontal cortex damage make utilitarian judgments in relation to harmful situations. This thesis will sustain that the dual-process theory have gained strong empirical support, especially from the research that has been made on patients with ventromedial prefrontal cortex damage. This thesis will also argue that some modifications needs to be made on the dual-process theory in order to make it stronger.

With the arrival of automated vehicles (AVs) on our streets virtually around the corner, this thesis explores advances in automated driving technology with a focus on ethical decision making in dilemmatic traf- fic situations. In a total of five publications, we take a multi-facetted approach to analyse and address the core challenges related to auto- mated ethical decision making in AVs. In publications one through three, we conduct a series of immersive virtual reality studies to analyze human behavior in traffic dilemmas, explore mathematical approaches to model the decision making process, investigate how the assessment methodology can affect moral judgment, and discuss the implications of these studies for algorithmic decision making in the real-world. In publication number four, we provide a comprehensive summary of the status quo of AV technology and legislation with regard to automated ethical decision making. Here, we discuss when and why ethical deci- sion making systems become necessary in AVs, review existing guide- lines for the behavior of AVs in dilemma situations, and compile a set of 10 demands and open questions that need to be addressed in the pursuit of a framework for ethical decision making in AVs. Finally, the basis for automated ethical decision making in AVs will be provided by accurate assessments of the immediate environment of the car. The pri- mary technology used to provide the required information processing of camera and LiDAR images in AVs is machine learning, and in particular deep learning. In publication five, we propose a form of adaptive acti- vation functions, addressing a central element of deep neural networks, which could, for instance, lead to increased detection rates of relevant objects, and thus help to provide a more accurate assessment of the AVs environment. Overall, this thesis provides a structured and compre- hensive overview of the state of the art in ethical decision making for AVs. It includes important implications for the design of decision mak- ing algorithms in practice, and concisely outlines the central remaining challenges on the road to a safe, fair and successful introduction of fully automated vehicles into the market.