Madison Coots

9 Jan 2025

So excited this finally out!! 🥳 Thread on our new paper 👇

Alex Chohlas-Wood@alexchohlaswood.com

NEW in Management Science! My coauthors and I came up with a new consequentialist approach to designing equitable algorithms. Instead of imposing fairness criteria on an algorithm (like equal false negative rates), we aim for good outcomes. More in the 🧵 below! (1/)

Madison Coots reposted

🚨 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...

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.

13 Dec 2024

We hope that our article provides a helpful overview of algorithmic fairness debates in healthcare. Please engage with us with any comments or questions!

Using lung cancer screening as an extended case study, we unpack these four categories of fairness concerns and discuss popular approaches for addressing them. Ultimately, we show that these approaches, if deployed, may in fact WORSEN outcomes for individuals across all groups.

13 Dec 2024

These concerns are not unique to the case of lung cancer and apply to the other case studies we discuss in the article, including VBAC calculators, CVD incidence and mortality models, kidney function (eGFR) equations, and healthcare need prediction models.

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

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

We hope that our work underscores the importance of foregrounding not only improvements in accuracy, but changes in *decisions and utility* in considering the use of race and ethnicity clinical decision-making.

annals.org/aim/article/...

annals.orgA Framework for Considering the Value of Race and Ethnicity in Estimating Disease Risk | Annals of Internal MedicineBackground: Accounting for race and ethnicity in estimating disease risk may improve the accuracy of predictions but may also encourage a racialized view of medicine. Objective: To present a decision ...

As a result, the overall clinical utility of race-aware models is surprisingly small. Context matters, but the benefits of race-aware models have likely been overstated.

Our study comes with several important caveats. Notably, in resource-constrained settings (e.g. organ transplants), race-aware models are expected to offer more substantial utility gains.

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.