But despite this miscalibration, *decisions* (e.g., to intervene in some way) based on race-aware vs. race-unaware risk estimates are largely the same, as very few individuals are close to the decision boundary.

We find that race-unaware risk estimates are indeed often *miscalibrated*, systematically over or underestimating risk for different groups. Such miscalibration is often cited as evidence that including race improves the quality of predictions for all groups.

Using race in medical risk assessments is hotly debated, with some arguing that doing so improves accuracy while others worry it reinforce pernicious attitudes. With @madisoncoots.com and colleagues, we identify a statistical twist that's been largely overlooked. 5harad.com/papers/race-...

Naviance is widely used by high school students to help them decide where to apply to college. In our new PNAS paper, Sabina Tomkins, Josh Grossman, Lindsay Page and I show it can inadvertently dissuade qualified students from applying to selective schools. www.pnas.org/doi/10.1073/...

Our paper "The Measure and Mismeasure of Fairness" — long in draft form — is now out in JMLR! We show that common error rate measures are often misleading indicators of algorithmic bias, and argue it's better to evaluate algorithms by looking at their effects. 5harad.com/papers/fair-...