  Educators are rapidly switching to remote proctoring and examination software
for their testing needs, both due to the COVID-19 pandemic and the expanding
virtualization of the education sector. State boards are increasingly utilizing
these software for high stakes legal and medical licensing exams. Three key
concerns arise with the use of these complex software: exam integrity, exam
procedural fairness, and exam-taker security and privacy. We conduct the first
technical analysis of each of these concerns through a case study of four
primary proctoring suites used in U.S. law school and state attorney licensing
exams. We reverse engineer these proctoring suites and find that despite
promises of high-security, all their anti-cheating measures can be trivially
bypassed and can pose significant user security risks. We evaluate current
facial recognition classifiers alongside the classifier used by Examplify, the
legal exam proctoring suite with the largest market share, to ascertain their
accuracy and determine whether faces with certain skin tones are more readily
flagged for cheating. Finally, we offer recommendations to improve the
integrity and fairness of the remotely proctored exam experience.

Cryptography and Security;Computers and Society