Facial recognition technology is improving by leaps and bounds. Some commercial software can now tell the gender of a person in a photograph.
When the person in the photo is a white man, the software is right 99 percent of the time.
But the darker the skin, the more errors arise — up to nearly 35 percent for images of darker skinned women, according to a new study that breaks fresh ground by measuring how the technology works on people of different races and gender.
These disparate results, calculated by Joy Buolamwini, a researcher at the M.I.T. Media Lab, show how some of the biases in the real world can seep into artificial intelligence, the computer systems that inform facial recognition.
One widely used facial-recognition data set was estimated to be more than 75 percent male and more than 80 percent white, according to another research study.
The new study also raises broader questions of fairness and accountability in artificial intelligence at a time when investment in and adoption of the technology is racing ahead.
The African and Nordic faces were scored according to a six-point labeling system used by dermatologists to classify skin types. The medical classifications were determined to be more objective and precise than race.
Then, each company’s software was tested on the curated data, crafted for gender balance and a range of skin tones. The results varied somewhat. Microsoft’s error rate for darker-skinned women was 21 percent, while IBM’s and Megvii’s rates were nearly 35 percent. They all had error rates below 1 percent for light-skinned males.