Nudging human drivers via implicit communication by automated vehicles: Empirical evidence and computational cognitive modeling

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage...

Meaningful human control: actionable properties for AI system development

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of...

Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid

People seem to hold the human driver to be primarily responsible when their partially automated vehicle crashes, yet is this reasonable? While the driver is often required to immediately take over from the automation when it fails, placing such high expectations on the driver to remain vigilant in partially automated driving is unreasonable....

Shift and Blend: Understanding the hybrid character of computing artefacts on a tool-agent spectrum

In the context of human-agent interaction, we see the emergence of computational artefacts that display hybridity because they can be experienced as tools and agents. In this paper we propose a tool-agent spectrum as an analytical lens that uses ‘intention’ as a central concept. This spectrum aims to clarify how a computational object can change...