MediumProxy Discrimination◐Demonstrated
Proxy / indirect discrimination via prompt features
Fairness & BiasDescription
Seemingly neutral input features (pincode, language, name) act as proxies for protected attributes, producing indirect discrimination.
Example scenario
Pincode used in a prompt becomes a proxy for caste/religion, skewing eligibility language.
Real-world evidence◐Demonstrated
The Gender Shades study empirically demonstrated that intersectional groups (e.g., darker-skinned women) suffered compounded error rates far exceeding single-axis assessments in commercial computer vision systems. BFSI-specific production incidents isolating intersectional blind spots as the documented cause have not been confirmed.
Primary mitigations
- Proxy-feature analysis
- fairness-through-awareness testing
- feature governance
- redaction of high-proxy inputs.
Detection signals
Proxy-correlation analysis; disparate-impact testing on neutral features.
Mitigating controls
7 Non-agentic controls
ZYC-FAIR-001Non-Agentic
Group Fairness
Fairness
ZYC-ETH-001Non-Agentic
Non-Maleficence
Ethical Governance
ZYC-ACCU-001Non-Agentic
Lexical Similarity
Accuracy
ZYC-FAIR-012Non-Agentic
Intersectional Parity
Fairness
ZYC-FAIR-002Non-Agentic
Individual Fairness
Fairness
ZYC-FAIR-006Non-Agentic
Contextual Fairness
Fairness
ZYC-FAIR-007Non-Agentic
Bias Mitigation - Post-processing
Fairness