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21/08/2026 4:55 pm
TL;DR: Using geolocation to infer missing race data in mortgage decisions can approximate disparities, but thresholding can overstate approval gaps and discard sample data, creating a fidelity problem for fairness measurement rather than solving it, according to Fiddler’s analysis. For IAM and governance teams, the lesson is that proxy-driven inference needs explicit validation before it is trusted in compliance, model risk, or decisioning workflows.
NHIMG editorial — based on content published by Fiddler: Identifying Bias When Sensitive Attribute Data Is Unavailable: Geolocation in Mortgage Data
Questions worth separating out
Q: How should teams use proxy data when protected attributes are missing?
A: Use proxy data only for controlled analysis, not as a substitute for ground truth.
Q: Why can threshold-based inference distort fairness results?
A: Thresholds exclude lower-confidence records, and those records are often not evenly distributed across the population.
Q: What do organisations get wrong about inferred sensitive attributes?
A: They often treat a derived attribute as if it were the same as directly collected data.
Practitioner guidance
- Validate proxy fidelity before reporting disparities Compare inferred outcomes against known self-reported attributes in a test sample and quantify how much the proxy shifts approval rates, error rates, and subgroup variance.
- Use probabilistic aggregation instead of hard labels Where possible, calculate fairness metrics from the full probability vector rather than collapsing records into a single inferred class with a threshold rule.
- Document threshold sensitivity as part of model governance Run the same fairness analysis across several cutoffs and record how sample size and disparity estimates change so reviewers can see whether the result is stable.
What's in the full article
Fiddler's full post covers the analytical detail this post intentionally leaves for the source:
- Step-by-step walkthrough of the geolocation matching method used to infer race from HMDA data
- Comparative examples of thresholded versus weighted estimators and how each changes disparity calculations
- Source references to Chen et al. and CFPB methodology for proxy-based fairness analysis
- The visual logic behind the sample-size trade-off created by stricter inference thresholds
👉 Read Fiddler's analysis of geolocation-based race inference in mortgage fairness data →
Geolocation proxies for race inference in mortgage models?
Explore further
21/08/2026 5:12 pm
Proxy fidelity is the real control issue, not proxy availability. The article shows that organisations can infer missing sensitive attributes from geolocation, but the statistical proxy is only useful if it preserves the meaning of the underlying fairness question. If the proxy changes the measured disparity, the governance output becomes a model of the proxy, not a model of the population. That is a measurement integrity problem, and it belongs in model risk review rather than being treated as a clever workaround.
A question worth separating out:
Q: How do you know if a fairness proxy is good enough?
A: A proxy is good enough only if it preserves the direction and rough magnitude of the disparity under analysis across multiple tests. If different thresholds, samples, or estimation methods produce materially different results, the proxy is too unstable for compliance-grade reporting. Stable results matter more than elegant methodology.
👉 Read our full editorial: Geolocation proxies can distort fairness analysis in mortgage data
