Demographic skews in training data create algorithmic errors

ALGORITHMIC BIAS is often described as a thorny technical problem. Machine-learning models can respond to almost any pattern--including ones that reflect discrimination. Their designers can explicitly prevent such tools from consuming certain types of information, such as race or sex. Nonetheless, the use of related variables, like someone's address, can still cause models to perpetuate disadvantage. Ironing out all traces of bias is a daunting task. Yet despite the growing attention paid to this problem, some of the lowest-hanging fruit remains unpicked.