Late last year, the European Union introduced the Artificial Intelligence Liability Directive (AILD) to "improve the functioning of the internal market by laying down uniform rules for certain aspects of non-contractual civil liability for damage caused with the involvement of AI systems." Bad AI is AI that isn't trustworthy--AI that is based on biased or incomplete data that then, in turn, could perpetuate harmful outcomes. And with AI expecting a compound annual growth rate of 20% by 2030--to reach nearly US $1.4 trillion--the technology, media and telecommunications (TMT) industry has a critical responsibility to not only develop the most trustworthy AI but also model the most trustworthy AI behavior to their business customers and society at large. While AI may have seemed like the stuff of science fiction, it has now entered the realm of reality and offers incredible potential to make businesses more competitive. According to Deloitte's AI Dossier, there are six key ways AI can help businesses create value: But while AI presents amazing potential for business value, AI has an equal amount of potential to go wrong.

As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI, however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling.