Bias and fairness
Does removing race from a cancer risk model make it fairer?
Open access · cc by · source: Europe PMC
Taking race and ethnicity out of a breast cancer risk model barely changed its overall accuracy but made its risk estimates wrong for Black and Asian women in opposite directions.
Study at a glance
- Design
- Cohort — Registry cohort of screening mammograms (2005-2017); BCSC advanced breast cancer logistic-regression risk model compared with and without race and ethnicity as a predictor
- N
- N=931186 · 931,186 women aged 40-74 contributing 3,294,431 annual or biennial screening mammograms
- Population
- US women aged 40-74 in routine annual or biennial mammography screening in Breast Cancer Surveillance Consortium registries
- Outcome
- Calibration (expected/observed ratio) and discrimination (AUC) by racial and ethnic group, and share of women classed as intermediate/high advanced cancer risk
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Overall AUC was almost unchanged (0.682 with race vs 0.677 without). But without race, risk was underestimated for Black women (expected/observed 0.61 among annual screeners) and overestimated for Asian women (1.28), whereas the original model was well calibrated for every group. The share of Black women flagged as intermediate/high risk fell from 58.5% to 24.1%, and among Black women who did develop advanced cancer, the share flagged fell from 75.3% to 47.5%; the share of Asian women flagged rose from 3.4% to 10.2%.
Methodology
The researchers used registry data on 931,186 women having routine screening mammograms to compare an existing model that predicts six-year risk of advanced breast cancer with and without race and ethnicity as an input. For each racial and ethnic group they checked calibration (whether predicted numbers of cancers matched observed numbers), discrimination (AUC), and how many women were placed in the intermediate/high-risk category that could prompt annual screening or extra imaging.
Limitations
The model is a logistic regression, so this is a lesson about removing a protected attribute rather than about a complex machine-learning system. Some groups were small, giving wide confidence intervals, and Pacific Islander women could not be analysed separately. The study did not measure harms such as false positives from extra screening, and it cannot say which risk thresholds are best or whether using the model improves outcomes.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
'Race-blind' is not automatically fair.
Removing race from a risk model can harm calibration for specific groups while leaving overall accuracy unchanged: AUC barely moved (0.682 vs 0.677), but risk was underestimated for Black women (expected/observed 0.61) and the share of Black women with advanced cancer flagged fell from 75.3% to 47.5%.
Evidence for the claim as stated.
Discoveries this paper informs or conflicts with
- Dropping race from a model can make it less fair, and fixing one gap can widen another
This paper informs this development.
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