POSTPONED: The Flinn Foundation Buffmire Lecture Featuring Ziad Obermeyer, MD

This event has been postponed. The College of Medicine – Tucson Dean's Office will announce a new date at a later time.

"Asking Algorithms (and Ourselves) the Right Questions"

Date: Thursday, March 26, 2020
Time: 11:30 a.m. – 1 p.m. (Lunch service at 11:30; lecture at noon)
Location: Forum, Health Sciences Innovation Building, HSIB 

Algorithms are moving out of the lab and into society, where they are asked to answer tough questions: which inmates to release, which job applicants to hire, which patients to treat. These questions cause algorithms to stumble, not so much because they are hard to answer, but because they are hard to ask.

Humans easily grasp the idea of finding 'the sickest patients' or 'the best applicants'. Algorithms, by contrast, are incredibly literal: they can only deal with a particular variable in a particular dataset. Research is now showing that the specific ways in which abstract ideas get translated into machine-answerable questions can distort algorithms, leading them to encode bias and error. I'll discuss one example of this from my own work, where a health algorithm created large-scale racial bias for tens of millions of patients, because it used health care costs as a proxy for health care needs. I'll also give two reasons to be optimistic about the future of algorithms. 

First, many biases are fixable, by paying close attention to seemingly small technical choices when building algorithms, as we demonstrated by collaborating with the company that made the biased algorithm. Second, by forcing us to write down exactly what we mean -- what question we are asking exactly -- algorithms can hold up a mirror to our own biases, allowing us to understand and correct them. Third, by developing critical assessment skills, clinicians can recognize situations where the information they receive from algorithms doesn’t match what they see in the patient’s condition.