Facial Recognition: Congress, the Judicial System and Demographics
History has shown us out of alignment congressional and judicial models costs. There is always a group that pays starting with the seizure of land from indigenous people, and continuing with enslaved people. This misalignment made laws to ban Chinese immigration, imprisoned and seize property from American citizens of Japanese heritage. This misalignment made it illegal for women to vote. The congressional data model is skewed. By numbers alone, a demographic shift should be reflected in the legislature with a slight lag. Historically, that has not happened.
It’s 2018, and the software design for facial recognition in the US literally does not recognize dark skin females because the developers used a dataset of 80% male and 75% white. With opportunities for Supreme Court justices, where is the conversation for the makeup of the court to track somewhat with demographics? The US Congress currently 80% white and 80% male for a country that is 63% white and 49% male. Doesn’t critical thinking sound the alarm? Does this not parallel the facial recognition dilemma? Joy Buolamwini, a Rhodes scholar at MIT Media labs found that facial recognition systems did not work as well for her as it did for others.
Moving forward, can the same be said of government? Government should be more attuned to the people with use it and the people it is used on you can’t have ethical government that’s not inclusive, and whoever is creating the laws is setting the standards. If those creating technologies³ continue to fail at diversity until called out, is government any different? I can’t help but ponder while the numbers may be for us, is the force against us?
¹facial recognition for gender
light skinned males – 1% error
light skinned females – 7% error
dark skin males – 12% error
dark skin females – 35 % error
²Erroneous decisions made from bad data are not only inconvenient, but also extremely costly. IBM looked at poor data quality costs in the United States and estimated that decisions made from bad data cost the US economy roughly $3.1 trillion dollars each year.