Deep neural networks (DNN) have made impressive progress in the interpretation of image data so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. From an ethical standpoint, the AI algorithm should take into account the vulnerability of objects or subjects on the street that ranges from “not at all”, e.g. the road itself, to “high vulnerability” of pedestrians. One way to take this into account is to define the cost of confusion of one semantic category with another and use cost-based decision rules for the interpretation of probabilities, which are the output of DNNs. However, it is an open problem how to define the cost structure, who should be in charge to do that, and thereby define what AI-algorithms will actually “see”. As one possible answer, we follow a participatory approach and set up an online survey to ask the public to define the cost structure. We present the survey design and the data acquired along with an evaluation that also distinguishes between perspective (car passenger vs. external traffic participant) and gender. Using simulation based F-tests, we find highly significant differences between the groups. These differences have consequences on the reliable detection of pedestrians in a safety critical distance to the self-driving car. We discuss the ethical problems that are related to this approach and also discuss the problems emerging from human–machine interaction through the survey from a psychological point of view. Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.
Successful knowledge integration, that is, systematic synthesis of unshared information, is key to suc-cess, but at the same time a challenging venture for teams with distributed knowledge collaborating online. For example, teams with heterogeneous knowledge often have only vague or even wrong ideas about who knows what. This situation is further complicated if the collaboration partners do not know each other and merely communicate online. Previous research has found meta-knowledge, that is, knowledge about one's own and the partner's knowledge areas, to be a promising but not yet sufficient-ly investigated approach to promote knowledge integration. With our experimental study we aimed to address this desideratum of research on the role of meta-knowledge in net-based collaborations. We "simulated" a chat-based collaboration between partners with heterogeneous knowledge by assigning specific information to students collaborating in dyads on a Hidden Profile task. To arrive at the correct joint solution for this task, collaborating partners had to pool their shared, but more importantly their unshared information. We compared two conditions: In the experimental condition meta-knowledge was promoted by providing the collaboration partners with self-presentations of each other's roles, which pointed to their unique fields of knowledge, while participants in the control condition did not receive this information. Results suggest a positive impact of the meta-knowledge manipulation on two key factors of collaboration: knowledge integration and construction of a transactive memory system (TMS).
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