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Madison Coots @madisoncoots.com · 09/01/2025

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

Reposted by Madison Coots

Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

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/)

A screenshot of the first page of our paper, Learning to Be Fair, showing the title and abstract.

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Madison Coots @madisoncoots.com · 13/12/2024

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

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Madison Coots @madisoncoots.com · 13/12/2024

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.

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Madison Coots @madisoncoots.com · 13/12/2024

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.

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Madison Coots @madisoncoots.com · 13/12/2024

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.

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Madison Coots @madisoncoots.com · 13/12/2024

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

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Madison Coots @madisoncoots.com · 13/12/2024

🚨 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...

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Madison Coots @madisoncoots.com · 05/12/2024

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.

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Madison Coots @madisoncoots.com · 05/12/2024

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.

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Madison Coots @madisoncoots.com · 05/12/2024

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.

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Madison Coots @madisoncoots.com · 05/12/2024

Further, these individuals also experience modest gains in utility from the use of a race-aware model. This is because, in shared decision-making contexts like the ones we consider, the utility of intervention is 0 at the decision threshold.

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Madison Coots @madisoncoots.com · 05/12/2024

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.

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Madison Coots @madisoncoots.com · 05/12/2024

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.

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Madison Coots @madisoncoots.com · 05/12/2024

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

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