There’s More to AI Bias Than Biased Data

Rooting out bias in artificial intelligence will require addressing human and systemic biases as well.  As a step toward improving our ability to identify and manage the harmful effects of bias in artificial intelligence (AI) systems, researchers at the U.S. National Institute of Standards and Technology (NIST) recommend widening the scope of where we look for the source of these biases — beyond the machine learning processes and data used to train AI software to the broader societal factors that influence how technology is developed.