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Overcoming Bias in Artificial Intelligence, Machine Learning

January 09, 2020 / Emily Sokol
SHARESHARESHARE

Artificial intelligence is often seen as the silver bullet to the healthcare industry’s numerous problems. Machine learning technologies have been shown to more quickly and accurately read radiology scans, identify high-risk patients, and reduce provider’s administrative burden.But recent studies have revealed the inherent bias perpetuated by using these algorithms in clinical practice, concerning many that these technologies are more harmful than helpful.Bias creeps in when developers use proxy measures for various health outcomes. End-users are often unaware of this and, as a result, can unintentionally give bias recommendations to patients.Philip Thomas, PhD, MS, assistant professor at the college of information and computer science at the University of Massachusetts Amherst, said the responsibility of fixing this problem lies in the hands of developers, not end-users.  We’re proposing the framework. We’re not saying use our algorithm as is,” he ex...