Biblio

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

In contrast to other studies in IC supply chain security where foundries are classified as either untrusted or trusted, a more realistic threat model is that the foundries are legally and economically obliged to perform trustworthy service, and it is the individual employees that introduce security risks. We call the above as the trusted foundry and untrusted employee (TFUE) model. Based on this model, we investigate new opportunities of establishing trustworthy operations in foundries made possible by double patterning lithography (DPL). DPL is used to setup two independent mask development lines which do not need to share any information. Under this setup, we consider the attack model where the untrusted employee(s) may try to insert Trojans into the circuit. As a countermeasure, we customize DPL to decompose the layout into two sub-layouts in such a way that each sub-layout individually expose minimum information to the untrusted employee.