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About this activity

Based on what you've learned, consider whether these scenarios represent a statistical fix and/or relational ethics.

Created by

Carrie Gan
Carrie Gan
United States

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Based on what you've learned, consider whether these scenarios represent a statistical fix and/or relational ethics.

by Carrie Gan
1

A hospital realizes its training dataset includes very few patients from a rural Native community. An engineer responds by oversampling the limited number of Native patients already in the data so that the model sees them more often. What kind of solution is this?

2

A development team pauses model building to meet with patients and clinicians from communities that have historically experienced medical bias. They ask how cultural mistrust, diagnostic delays, or access barriers shape the meaning of certain data points before they choose model features. What kind of solution is this?

3

A model shows higher false-negative rates for Black patients. To correct this, the team adjusts the model threshold until the error rates are equal across races. What kind of solution is this?

4

A developer notices the model predicts “health need” using total healthcare spending. Instead of correcting the numbers, the team asks whether spending reflects structural inequities in access and begins searching for clinical indicators that measure actual illness severity. What kind of solution is this?

5

Patients report the algorithm consistently underrates symptoms related to chronic pain. The hospital creates a standing advisory group of affected patients who now provide structured feedback on model performance across updates. What kind of solution is this?

6

Engineers notice extreme lab values among homeless patients and classify them as “outliers,” removing them from the dataset to stabilize model performance. What kind of solution is this?

7

A team equalizes error rates, and brings clinicians and community members together to review model assumptions before deployment. What kind of solution is this?

Choose one or more answers

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Oversampling changes the numbers but does not examine why the community was missing, who was excluded from data collection, or what social conditions affected access to care.

The team is seeking context, examining relationships, and grounding model choices in lived experience.

Equalizing error rates improves metrics but does not explore why the errors occur or whether the model’s structure reinforces inequities.

This approach questions the meaning of the variable itself and considers how racism shapes the proxy.

The solution centers patient experience and builds long-term accountability.

Removing outliers treats structural harm as statistical noise instead of understanding why those values appear.

This shows that relational ethics does not replace technical methods; it reframes how they are used.

 
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