Studying teaching behavior in controlled conditions is difficult. It seems intuitive that a human learner might have trouble reliably recreating response patterns over and over in interaction. A robot would be the perfect tool to study teaching behavior because its actions can be well controlled and described. However, due to the interactive nature of teaching, developing such a robot is not an easy task. As we will show in this review, respective studies require certain robot appearances and behaviors. These mainly (...) should induce teaching behavior in humans, be interactive, match the study design, and be realizable in terms of effort. We discuss how remote controlling of the robot or simulating robot capabilities is used as an option. With this review, we introduce the field of research on studying human teaching behavior with robots as a tool in the experimental design. We will provide a structured overview of existing work, and identify main challenges of employing robots in such studies. (shrink)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To (...) achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)