We conclude by arguing for an alternative framework for the design of equitable algorithms that moves beyond scrutinizing narrow statistical metrics and instead foregrounds health outcomes and utility and clarifies important trade-offs.

For each algorithm, we organize the fairness concerns into a taxonomy of four broad categories:
1️⃣ Inclusion/exclusion of race and ethnicity as inputs
2️⃣ Unequal decision rates across groups
3️⃣ Unequal error rates across groups
4️⃣ Label bias

🚨 Excited to share our new article in @annualreviews.bsky.social. Working with Kristin Linn, @5harad.com, Amol Navathe, and Ravi Parikh, we examine the fairness debates of seven prominent and controversial healthcare algorithms.🧵 madisoncoots.com/files/racial...

Yet, despite this miscalibration, clinical decisions (e.g., screening or treatment recommendations) differ between race-aware and race-unaware models for only a small fraction of individuals (~5%). The individuals whose decisions flip are those closest to the decision threshold.

Using cardiovascular disease, breast cancer, and lung cancer as case studies, we show that race-unaware models are often miscalibrated—underestimating risk for some groups and overestimating it for others. This finding is consistent with evidence cited in support of the use of race-aware models.

The use of race in clinical risk models is heavily debated. While race-aware models can be more accurate, some are concerned about reinforcing racialized views of medicine. In our paper, we offer a new perspective on this debate. 🧵👇https://annals.org/aim/article/doi/10.7326/M23-3166