However, further research is required to examine how to best form these human-machine teams to carry out face matching tasks. Current projects aim to identify benchmark levels of human and algorithm face matching performance on a dataset of image pairs and explore variations in types of errors made by humans and AI. We will be investigating how we can calibrate trust between humans and AI to facilitate optimal human-machine team performance. Understanding the effect on trust can help people make quicker, more effective, and more accurate decisions with the help of AI and help minimize the risk of errors and misidentification in applied settings.