Are crash-avoidance detectors less accurate with people of color?
Are automated crash-avoidance systems, critical to self-driven vehicles, worse at detecting people with darker skin?
Research at Georgia Tech University suggests the answer is yes, and now U.S. Rep. Bobby Rush, D-Ill., says he’s making progress with a bill to mandate a federal study of whether systems indeed are a particular threat to people of color.
Under Rush’s bill, the U.S. Department of Transportation would be directed to evaluate how accurately crash-avoidance systems detect pedestrians, bikers and others with darker skin compared to those with lighter skin. The measure may be added to a larger infrastructure package that’s up for a vote in the full House this week.
“I have long supported the development of autonomous and semi-autonomous vehicles,” Rush said in a statement. “But we have to be absolutely certain on a federal level that these vehicles are equipped to protect all Americans equally” as more and more self-driven vehicles hit the road.
“The Crash Avoidance System Evaluation Act will bolster our understanding of how life-saving, crash-avoidance technologies can be better implemented,” said Indiana GOP U.S. Rep. Larry Bucshon, who is co-sponsoring the bill.
The Georgia Tech study concluded that standard technologies “appear to exhibit higher precision” in detecting people with lighter skin shades. “We hope this study provides compelling evidence of the real problem that may arise if this source of capture bias is not considered before deploying these sort of recognition models,” the report said.
Rush said his bill is backed by groups including the Center for Auto Safety, the National Safety Council and the League of American Bicyclists.