Significant advances in artificial intelligence (AI) over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used 'off label' and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study.
