Towards Better Trust in Human-Machine Teaming through Explainable Dependability

The human-machine teaming paradigm is increasingly widespread in critical domains, such as healthcare and domestic assistance. The paradigm goes beyond human-on-the-loop and human-in-the-loop systems by promoting tight teamwork between humans and autonomous machines that collaborate in the same physical space. These systems are expected to build a certain level of trust by enforcing dependability and exhibiting interpretable behavior. We present emerging results in this direction, with a novel framework aiming at achieving better trust in human-machine teaming leveraging formal analysis, as well as eXplainable AI. We illustrate our approach and the emerging results with an example from the healthcare domain.