Multiple academic studies continue to show that facial recognition systems are significantly more likely to misidentify dark female faces than bright male faces. Nevertheless, the technology is expanding into retail security, border control, and employment tools.
“The average accuracy appears to be good because the system works best with the best trained groups,” said a technical researcher at the University of London.
In reality, this means a false alarm. Verification has been delayed. Additional scrutiny framed as technical error rather than bias.
Retailers claim efficiency. Police are speeding. But speed without accuracy puts the burden on the same people over and over again.
We’ve seen this before with credit scoring and predictive policing. Automation does not eliminate bias. Scale quietly.
Technology is not neutral simply because it is mathematical. It reflects the world that built it.
If a system cannot reliably recognize you, it should not be trusted to judge you.
Innovation does not automate inequality, it reduces it.
Source: Pride Magazine – www.pridemagazine.com
