This page is a fallback for search engines and cases when javascript fails or is disabled.
Please view this card in the library, where you can also find the rest of the plot4ai cards.
Could the system be using proxy variables that reflect sensitive attributes or lead to indirect discrimination?
Could the system be using proxy variables that reflect sensitive attributes or lead to indirect discrimination?
Proxy variables are features used as stand-ins for harder-to-measure characteristics. While proxies can be useful for model performance, they may be highly correlated with sensitive attributes such as race, gender, religion, age, or socioeconomic status. This can lead to indirect or proxy discrimination, where individuals from protected groups are disproportionately harmed despite sensitive data not being explicitly included.
For example, ZIP code, school name, or browsing history may function as proxies for race or income level. In such cases, the system might appear 'neutral' but still replicate or amplify historical inequalities. Proxy bias is especially insidious because it is often unintentional and hidden in seemingly innocuous variables.
Generative models can also internalize and reproduce these biases in subtle ways, such as generating different responses for identical inputs that differ only by proxy cues.
If you answered Yes then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Audit datasets and model features for correlations between input variables and sensitive attributes, even if the latter are not explicitly included. Use statistical techniques (e.g., mutual information, conditional independence tests) to detect proxy relationships.
- Where lawful and ethical, include sensitive features during training or evaluation (under a fairness-through-awareness approach) to test and correct for bias.
- Avoid using proxies that carry high risk of discrimination unless they are strictly necessary, legally justified, and subject to fairness constraints.
- Use fairness metrics (e.g., demographic parity, equal opportunity, calibration) to evaluate disparate impact across groups, and simulate decisions under different population assumptions.
- Apply model explainability tools (e.g., SHAP, LIME) to identify when proxy features are driving predictions.
- Include domain experts, ethicists, and affected stakeholders in feature selection and fairness reviews.
- Maintain documentation of proxy risks and mitigation decisions as part of your model cards or algorithmic accountability reports.