Hospitals and health care companies are increasingly tapping experimental artificial intelligence tools to improve medical care or make it more cost-effective. At best, that technology has the potential to make it easier to detect and diagnose diseases, streamline care, and even eliminate some forms of bias in the health care system. But if it's not designed and deployed carefully, AI could also perpetuate existing biases or even exacerbate their impact. "Badly built algorithms can create biases, but well-built algorithms can actually undo the human biases that are in the system," Sendhil Mullainathan, a computational and behavioral science researcher at the University of Chicago's Booth School of Business, told STAT's Shraddha Chakradhar at the STAT Health Tech Summit this month. Mullainathan also spoke with STAT about about the importance of communication in developing AI tools, the data used to train algorithms, and how AI could improve care.

It has nothing to do with any of the things you listed. Machine learning and pattern recognition basically come down to learning a model of the dataset and then predicting something based on that model. If the model is "biased" then it's because the dataset was "biased". I don't understand what you are getting at when you talk about the black/white/male/female stuff. Black/white/male/female are just arbitrary labels defined by you.