<https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artifi...> 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. [...] In her newly published paper <http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf>, which will be presented at a conference <https://fatconference.org/> this month, Ms. Buolamwini studied the performance of three leading face recognition systems — by Microsoft, IBM and Megvii of China — by classifying how well they could guess the gender of people with different skin tones. These companies were selected because they offered gender classification features in their facial analysis software — and their code was publicly available for testing. She found them all wanting. To test the commercial systems, Ms. Buolamwini built a data set of 1,270 faces, using faces of lawmakers from countries with a high percentage of women in office. The sources included three African nations with predominantly dark-skinned populations, and three Nordic countries with mainly light-skinned residents. 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. Ms. Buolamwini shared the research results with each of the companies. IBM said in a statement to her that the company had steadily improved its facial analysis software and was “deeply committed” to “unbiased” and “transparent” services. This month, the company said, it will roll out an improved service with a nearly 10-fold increase in accuracy on darker-skinned women. Microsoft said that it had “already taken steps to improve the accuracy of our facial recognition technology” and that it was investing in research “to recognize, understand and remove bias.” Ms. Buolamwini’s co-author on her paper is Timnit Gebru, who described her role as an adviser. Ms. Gebru is a scientist at Microsoft Research, working on its Fairness Accountability Transparency and Ethics in A.I. <https://www.microsoft.com/en-us/research/group/fate/>group. []