The University of Michigan Ford School of Public Policy hosted a webinar Wednesday afternoon about the disproportionate impact of facial recognition technology on students of color. The event was presented by the Science, Technology, and Public Policy Program.

According to the team’s report, facial recognition technology is consistently inaccurate at recognizing Black people,  Indigenous people and people of color. These children are more likely to be marked absent from school, rejected from class or identified as intruders when facial recognition technology is used, said U-M alum Claire Galligan, one of the team’s researchers. 

Hannah Rosenfeld, a Public Policy graduate student and a researcher in the program, said inaccuracies are introduced in the learning phase of facial recognition technology because companies test the technology on a sample set of people who are majority white and male. Because of the unrepresentative sample set, the technology is much less accurate when used to identify women or people of color. 

Rosenfeld said even if people confirm the technology’s results, human confirmation is also subject to bias. The team’s report mentions frequently that people are less likely to identify someone of a different race accurately.  

Galligan noted that human technology does not exist in a vacuum. She compared law enforcement stop and frisk policies to facial recognition technology, writing in an email to The Daily that both serve as “a crime control strategy that disproportionately targets people of color.”

The team’s report also argued that facial recognition technology invites unwelcome surveillance into classrooms. Galligan said the use of similar surveillance technology, such as closed-circuit television cameras, fails to acknowledge student privacy. 

Galligan said school administrators were more likely to use surveillance footage to punish students for violating dress codes or being tardy than for the original intent of creating a safe school environment.

“Students said (cameras) made them feel powerless, criminalized and less safe in schools,” Galligan said.

The team also concluded that facial recognition technology punishes nonconformity in schools, presenting the Aadhaar Biometric System in India as a case study. This system requires biometric data such as fingerprints or iris scans to access programs such as welfare and pensions, which sets up barriers for citizens with damaged fingerprints or eyes.

Galligan said facial recognition technology that assumes homogeneity in a society disadvantages students, especially those who have physical disabilities or are gender non-conforming. 

Rosenfeld mentioned that because children have no choice but to use these technologies in both in-person and online classroom settings, it has the potential to open a new private market for police and government surveillance work. 

School administrators are not the only people who can use this technology to their benefit, Rosenfeld said. Law enforcement can also subpoena information to aid in police work without the consent of parents or students, which Rosenfeld said could have negative implications. 

Overall, the team’s report said facial recognition technology should not be used in any capacity in educational systems. 

STPP director Shobita Parthasarathy, the lead of the project, said there is no clear national policy regulating these technologies, so the report included local and state policy recommendations.

In an email to The Daily, Rosenfeld wrote she could not think of a situation in which the benefits of facial recognition technology outweigh the drawbacks. 

“Traditionally, there’s this idea out there that you can’t regulate technology until it is already out in the world because it is impossible to predict what might happen,” Rosenfeld wrote. “However, there is a long history of research in the field of Science, Technology, and Society that demonstrates that in fact, technology is more predictable than we like to think.”

Daily Staff Reporters Isabella Preissle and Meghana Lodhavia can be reached at and 

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