Failure Modes: Proxy Leakage and Feedback Loops

Two critical failure modes undermine fairness efforts even with rigorous metric tracking. Proxy leakage occurs when models relearn protected attributes from correlated features, circumventing attempts to exclude sensitive data. Feedback loops arise when model predictions influence future training data, creating self reinforcing bias. Both require specialized detection and mitigation beyond standard fairness metrics.

Proxy leakage is pervasive because many features correlate with protected attributes. Removing race from a credit model does not prevent learning race when ZIP code, first name, or shopping patterns are available. A model can achieve 80% to 90% accuracy predicting race from these proxies alone. Intersections amplify the signal; combinations of three to five seemingly neutral features often encode protected attributes perfectly. Adversarial tests quantify leakage by training a separate model to predict sensitive attributes from the main model's learned representation. If this adversary achieves above 60% accuracy compared to random baseline, the representation contains significant protected information.

Feedback loops create selective labels and reinforcement. In credit and justice settings, outcomes are only observed for approved applicants or released individuals. A model that denies Group B at higher rates sees fewer Group B outcomes in future training data, further reducing estimated creditworthiness for that group. This compounds over iterations, with disparities growing by 5 to 10 percentage points per retraining cycle. Ad ranking systems face engagement feedback loops: underexposed creators have fewer impressions to generate clicks, leading to lower predicted CTR and further underexposure. After 10 ranking cycles without intervention, initially equal creators can diverge to 10x difference in exposure.

Mitigation combines exploration and counterfactual estimation. Randomized audits approve a small fraction of denied applicants, typically 1% to 5%, to observe outcomes and correct selective label bias. Exploration in ranking reserves 5% to 10% of impressions for random or diversity promoting allocation. Counterfactual methods like inverse propensity scoring reweight training examples by the inverse of their selection probability, upweighting underexposed items. Production systems at major platforms run quarterly deep audits that test for proxy leakage across 50+ feature combinations and monitor feedback loop metrics like exposure Gini coefficient, which increases from 0.4 to 0.7+ when loops are active.

💡 Key Takeaways

•Proxy leakage: ZIP code plus purchase history predicts race with 80% to 90% accuracy, even when race explicitly excluded, allowing models to relearn protected attributes

•Adversarial testing: Train separate model to predict sensitive attributes from representation, above 60% accuracy indicates leakage, run quarterly on 50+ feature combinations

•Selective labels: Credit and justice models only observe outcomes for approved cases, creating 5 to 10 percentage point disparity growth per retraining cycle without intervention

•Exploration tax: Reserve 1% to 5% of decisions for randomized approval to observe denied outcomes, or 5% to 10% of ranking impressions for diversity exploration

•Feedback loop amplification: Initially equal creator groups diverge to 10x exposure difference after 10 ranking cycles, tracked via exposure Gini coefficient rising from 0.4 to 0.7+

•Counterfactual correction: Inverse propensity scoring reweights training examples by 1 divided by selection probability, upweights underexposed items to break reinforcement

📌 Examples

Amazon resume screening relearned gender from college name, sports, and word choice, achieving 85% gender prediction accuracy despite excluding gender attribute

Spotify playlist recommendations tracked exposure Gini coefficient per artist demographic, implemented 10% exploration slot and saw coefficient drop from 0.68 to 0.51 over 3 months

Bank credit model implemented 2% randomized approval of denials, observed 8 percentage point higher repayment rate than model predicted for denied Group B applicants, corrected selective label bias

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