Evidence/algorithm bias – Ziad Obermeyer
October 7, 2020
Drawing on lessons from his work on algorithmic bias, Ziad Obermeyer talks about how seemingly small choices can snowball into large biases. In particular, that a subtle error—the conflation of health dollars with health needs—distorts both health algorithms and health policy, and discuss how to avoid that trap.
![](https://abimfoundation.org/wp-content/uploads/2020/10/2020-10-07-14_43_18-Learning_Action-Session_-Evidence_algorithm-bias-Ziad-Obermeyer-Research-Y.png)
Drawing on lessons from his work on algorithmic bias, Ziad Obermeyer talks about how seemingly small choices can snowball into large biases. In particular, that a subtle error—the conflation of health dollars with health needs—distorts both health algorithms and health policy, and discuss how to avoid that trap.