" Phrenology" has an old-fashioned ring to it. It sounds like it belongs in a history book, filed somewhere between bloodletting and velocipedes. We'd like to think that judging people's worth based on the size and shape of their skulls is a practice that's well behind us. However, phrenology is once again rearing its lumpy head, this time under the guise of technology.

In recent years, machine-learning algorithms have seen an explosion of uses, legitimate and shady. Several recent applications promise governments and private companies the power to glean all sorts of information from people's appearances. Researchers from Stanford University built a" gaydar" algorithm that they say can tell straight and gay faces apart more accurately than people can. The researchers indicated that their motivation was to expose a potential privacy threat, but they also declared their results as consistent with the" prenatal hormone theory" that hypothesizes that fetal exposure to androgens helps determine sexual orientation; the researchers cite the much-contested claim that these hormone exposures would also result in gender-atypical faces. Several startups claim to be able to use artificial intelligence (AI) to help employers detect the personality traits of job candidates based on their facial expressions. In China, the government has pioneered the use of surveillance cameras that identify and track ethnic minorities. Meanwhile, reports have emerged of schools installing camera systems that automatically sanction children for not paying attention, based on facial movements and microexpressions such as eyebrow twitches. University students taking online exams monitored by proctoring algorithms not only have to answer the questions, but also maintain the appearance of a student who is not cheating. These algorithms reportedly make false accusations against students with disabilities who move their faces and hands in atypical ways, and Black students have indicated that they have been required to shine bright lights in their faces so as to have their features detected at all.