Biased algorithms
Commenting on the study, Jonathan Singer, PhD, LCSW, associate professor at Loyola University, Chicago, described it as an “important contribution because it points to a systemic problem and also to the fact that the algorithms we create are biased, created by humans, and humans are biased.”
Although the study focused on the health care system, Dr. Singer believes the findings have implications for individual clinicians.
“If clinicians may be biased against identifying suicide risk in Black and Native American patients, they may attribute suicidal risk to something else. For example, we know that in Black Americans, expressions of intense emotions are oftentimes interpreted as aggression or being threatening, as opposed to indicators of sadness or fear,” noted Dr. Singer, who is also president of the American Academy of Suicidology and was not involved with the study,
“Clinicians who misinterpret these intense emotions are less likely to identify a Black client or patient who is suicidal,” Dr. Singer said.
The research was supported by the Mental Health Research Network from the National Institute of Mental Health. Dr. Coley has reported receiving support through a grant from the Agency for Healthcare Research and Quality. Dr. Singer reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.