Gender Bias in Artificial Intelligence

This is a summary of a law review article published in the Minnesota Journal of Law, Science & Technology. The full text is available here

The tech world's adage "garbage in, garbage out" is increasingly more important as we rely on machine learning and algorithms that take data to predict or diagnose health issues. So much of our health data has gender bias baked in - for example, we know that women's pain is under treated. Using existing biased data to help automate processes will make them even more biased.

Patients, providers, regulators and industry must work together to ensure that tools protect patients and provide accurate, unbiased, information.