Communications of the ACM
These New Tools Could Make AI Vision Systems Less Biased
Sony's team developed a tool that expands the skin-tone scale into two dimensions, measuring both skin color (from light to dark) and skin hue (from red to yellow). Sony is making the tool freely available online.
Credit: Stephanie Arnett/MIT Technology Review/Getty
Computer vision systems are everywhere. They help classify and tag images on social media feeds, detect objects and faces in pictures and videos, and highlight relevant elements of an image. However, they are riddled with biases, and they're less accurate when the images show Black or brown people and women. And there's another problem: the current ways researchers find biases in these systems are themselves biased, sorting people into broad categories that don't properly account for the complexity that exists among human beings.
Two new papers by researchers at Sony and Meta propose ways to measure biases in computer vision systems so as to more fully capture the rich diversity of humanity. Both papers will be presented at the computer vision conference ICCV in October. Developers could use these tools to check the diversity of their data sets, helping lead to better, more diverse training data for AI. The tools could also be used to measure diversity in the human images produced by generative AI.
Traditionally, skin-tone bias in computer vision is measured using the Fitzpatrick scale, which measures from light to dark. The scale was originally developed to measure tanning of white skin but has since been adopted widely as a tool to determine ethnicity, says William Thong, an AI ethics researcher at Sony. It is used to measure bias in computer systems by, for example, comparing how accurate AI models are for people with light and dark skin.
But describing people's skin with a one-dimensional scale is misleading, says Alice Xiang, the global head of AI ethics at Sony. By classifying people into groups based on this coarse scale, researchers are missing out on biases that affect, for example, Asian people, who are underrepresented in Western AI data sets and can fall into both light-skinned and dark-skinned categories. And it also doesn't take into account the fact that people's skin tones change. For example, Asian skin becomes darker and more yellow with age while white skin becomes darker and redder, the researchers point out.
From MIT Technology Review
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