How bad facial recognition software gets Black people arrested

How bad facial recognition software gets Black people arrested

The riots all over the US are reminding us, once again, of the blatant oppression of people of color by predominantly White people. But while police murders are in focus right now, we shouldn’t be forgetting about the subtler tools that the justice system uses to ruin Black lives.

The riots all over the US are reminding us, once again, of the blatant oppression of people of color by predominantly White people. But while police murders are in focus right now, we shouldn’t be forgetting about the subtler tools that the justice system uses to ruin Black lives.

For one, there are the numerous Black and Brown people who die after being arrested. While federal officials have failed to gather the data, independent resources show that the disparity is real.

Then there is the fact that people of color get harsher sentencing upon trial. To state the obvious, this is a consequence of racial bias among predominantly White criminal judges.

On top of all that, there is the fact that Black and Brown people get arrested way more often. While Black people make up 13% of the average population, they make up 33% of US prisons, as of 2017. White people, or 72% of the population, only make up 30% of prisons. Statistically speaking, a Black person is therefore six times more likely to be incarcerated than a White person.

It is therefore quite obvious that Black and Brown people get oppressed by the police and the justice system due to racial bias. What is less obvious is that predominantly White software developers contribute to this crime.

Algorithms mismatch dark-skinned people 10 times more often

recent study by the National Institute of Standards and Technology (NIST) confirms what researchers have said all along: Facial recognition serves you well as long as you’re a white male — all others lose out.

One method that is important to courts is one-to-one matching. This means that you query whether two different photos depict the same person, which can be useful to identify a suspect that you’ve caught on video, for example. The study finds that there are 10 to 100 times more false positives for Black or Asian Americans than for Caucasian Americans, depending on the algorithm. In other words, if an algorithm is presented with two images from two different people of color, they look pretty much the same to the computer.

The number of false positives is about the same, regardless of non-Caucasian race, in US-developed algorithms. Apart from African and Asian Americans, this also included Native American, American Indian, Alaskan Indian and Pacific Islanders in the study.

Some researchers, among them Patrick Grother from NIST itself, have mentioned that there could be more diversity among the faces of Caucasians than among those of Africans or Asians. But this doesn’t hold true: The NIST study also finds that algorithms that were developed in Asia managed to identify Asians about as accurately as Caucasians. The root cause seems to lie much more within the algorithm itself, or in the dataset of images that it learns from.

Demonstrant wearing a Black Lives Matter t-shirt

Let’s not get these people falsely accused. Photo by Clay Banks on Unsplash

This lack in accountability for facial recognition isn’t only bemoaned by the NIST. For example, researchers Joy Buolamwini from MIT and Timnit Gebru from Stanford University found similar disparities regarding race and gender. It’s worth mentioning that both scientists are Black women themselves — two of the very few that work in Artificial Intelligence.

All these disparities wouldn’t be as bad if facial recognition were a toy made by White people for White people. The problem is that these algorithms are used on ethnic groups that had no say in their development and implementation. For example, they’re more likely to get wrongfully incarcerated because a machine made a mistake due to the color of their skin. As Buolamwini puts it, “technology should be more attuned to the people who use it and the people it’s used on.”

towards-data-science facial-recognition blacklivesmatter racism software data science

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