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21/08/2026 4:54 pm
TL;DR: When sensitive attribute data is unavailable, geography-only proxy methods may be used to approximate race, according to Fiddler, using a 2017 HMDA mortgage sample to test approval-rate disparities. The post shows how the absence of direct labels makes fairness measurement fragile, so teams need stronger governance around proxy selection, validation, and escalation thresholds.
NHIMG editorial — based on content published by Fiddler: Identifying Bias When Sensitive Attribute Data is Unavailable
By the numbers:
- The disparity in approval rate relative to the White or Asian/Pacific Islander group is 7% for Hispanic/Latino applicants and 15% for Black/African American applicants.
Questions worth separating out
Q: What goes wrong when teams use proxy data to assess bias?
A: Proxy data can blur the difference between the protected attribute and the signal used to infer it, which means the reported disparity may reflect proxy error as much as the underlying decision process.
Q: When should organisations rely on inferred attributes in fairness reviews?
A: Only when direct sensitive-attribute data is unavailable and the organisation can quantify the proxy’s error against a labelled benchmark.
Q: What do security and governance teams often get wrong about bias measurement?
A: They often treat a statistically neat proxy as if it were the actual attribute and then skip uncertainty analysis.
Practitioner guidance
- Define validation thresholds for proxy inference Set explicit error tolerances for geography-only or other inferred sensitive-attribute methods before using them in bias reporting, and require a labelled benchmark to prove the method is still usable.
- Separate outcome disparity from causal conclusions Write review procedures that treat approval-rate gaps as escalation triggers, then require policy, feature, and workflow analysis before any compliance conclusion is drawn.
- Keep self-reported attributes as a benchmark dataset Retain a controlled, privacy-governed source of self-reported race, ethnicity, or gender data so proxy methods can be tested against ground truth rather than assumed to be accurate.
What's in the full article
Fiddler's full blog covers the methodological detail this post intentionally leaves for the source:
- Step-by-step explanation of the geography-only inference approach used to approximate race from HMDA records
- Full description of the sample filtering rules that reduced 300,000 applications to roughly 200,000 records
- Discussion of the figures comparing approval-rate distribution across race groups and the U.S. population
- Reference trail to the cited Chen et al. and CFPB methodology papers for readers validating the research design
👉 Read Fiddler's analysis of bias detection when sensitive attribute data is unavailable →
Bias assessment without sensitive data: where does the proxy approach fail?
Explore further
21/08/2026 5:12 pm
Proxy fairness is a measurement-control problem, not just an ML problem. When organisations infer protected attributes from geography or other weak signals, they are building the control evidence on top of an approximation. That makes governance dependent on proxy quality, sample composition, and the stability of the inference method. For IAM-adjacent and identity-heavy programmes, the lesson is direct: if the measurement layer is weak, the fairness or trust decision built on it is weak too.
A question worth separating out:
Q: How should teams respond when outcome gaps appear in decisioning?
A: Treat them as a trigger for root-cause analysis, not as proof of discrimination or proof of innocence. Teams should review the features used, the policy thresholds applied, data quality, and whether the proxy method itself is introducing distortion before deciding on remediation.
👉 Read our full editorial: Bias assessment without sensitive data exposes measurement gaps
