Unmanned Aerial Systems (UASs) are continuing to proliferate rapidly [1]. Quantitative risk assessment for UAS operations, both a-priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. This paper reports the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of this research. This symposium was comprised of experts in UAS law, insurance, regulations, operations, and research discussing the direction of the UAS industry and the necessary steps to move the industry forward. Those results motivate the remainder of this research including the novel application of Dempster-Shafer networks using auto-updating transition matrices to the problem of UAS risk analysis and decision-making. This research trains a DS network based on simulated operation data, tests the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and explores how this system would extend to the full UAS ecosystem as discussed in the 2018 UAS safety symposium. Conclusions are drawn with respect to the research performed, and additional research tangents are proposed.