Everyone agrees that autonomous cars ought to save lives. Even if the cars do not live up to the most optimistic estimates of eliminating 90% of traffic fatalities [1], eliminating at least some traffic fatalities is one of the key promises of automated driving. Indeed, the first two principles of the German Ethics Code for Automated and Connected Vehicles lead with this goal as a normative imperative [2].The primary purpose of partly and fully automated transport systems is to improve safety for all road users. The licensing of automated systems is not justifiable unless it promises to produce at least a diminution in harm compared with human driving [...].

On the Web, there is always a need to aggregate opinions from the crowd (as in posts, social networks, forums, etc.). Different mechanisms have been implemented to capture these opinions such as Like in Facebook, Favorite in Twitter, thumbs-up/-down, flagging, and so on. However, in more contested domains (e.g., Wikipedia, political discussion, and climate change discussion), these mechanisms are not sufficient, since they only deal with each issue independently without considering the relationships between different claims. We can view a set of conflicting arguments as a graph in which the nodes represent arguments and the arcs between these nodes represent the defeat relation. A group of people can then collectively evaluate such graphs. To do this, the group must use a rule to aggregate their individual opinions about the entire argument graph. Here we present the first experimental evaluation of different principles commonly employed by aggregation rules presented in the literature. We use randomized controlled experiments to investigate which principles people consider better at aggregating opinions under different conditions. Our analysis reveals a number of factors, not captured by traditional formal models, that play an important role in determining the efficacy of aggregation. These results help bring formal models of argumentation closer to real-world application.

This position paper addresses the question of why, whilst the human brain is apparently geared to process information of mixed polarities, human reasoners sometimes fail to deal appropriately with simple instances of mixed evidence or mixed prospects. From a dual-process perspective of thinking, two mental routes for bipolar information processing are identified. One is biologically acquired and evolution tied, and the other is the product of a cultural elaboration of rational norms. In between the two routes, a blind spot accounts for failures of bipolar information processing. © 2008 Wiley Periodicals, Inc.

Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Prospect Theory. However, contraryto the latter framework that presupposes genuine numerical assessments, human agents often decide on the basis of an ordinal ranking of the pros and the cons, and by focusing on the most salient arguments. In other terms, the decision process is qualitative as well as bipolar. In this article, based on a bipolar extension of possibility theory, we define and axiomatically characterize several decision rules tailored for the joint handling of positive and negative arguments in an ordinal setting. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case, and consequently suffer from poor decisive power. More decisive rules that refine the former are also proposed. These refinements agree both with principles of efficiency and with the spirit of order-of-magnitude reasoning, that prevails in qualitative decision theory. The most refined decision rule uses leximin rankings of the pros and the cons, and the ideas of counting arguments of equal strength and cancelling pros by cons. It is shown to come down to a special case of Cumulative Prospect Theory, and to subsume the ``Take the Best'' heuristic studied by cognitive psychologists.