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Base-Rate Fairness Impossibility Lab

Interactive 3D demonstration of the fairness impossibility result: when two groups have different base rates, a classifier that is equally accurate for both (equal FPR/TPR) cannot also be equally calibrated (equal PPV) — see the trade-off live.

Data Science3DAdvanced60 FPS

A machine learning classifier scores two demographic groups using exactly the same underlying risk model — yet the groups have different true base rates for the outcome being predicted. This simulator renders both populations as 3D point clouds along a shared score axis and lets you set each group's base rate and the decision threshold(s), then watches equalized-odds metrics (FPR, TPR) and the calibration metric (PPV) update live. The result reproduces a real mathematical fact known as the fairness impossibility theorem: when base rates differ, equal accuracy across groups and equal calibration across groups cannot both hold at once, except in the trivial case where the base rates already match.

⚙ Under the hood

See the fairness impossibility theorem in action: two demographic groups scored by the same risk model but with different true base rates cannot have both equal accuracy (FPR/TPR) and equal calibration (PPV) at once.

fairnessmachine-learningbiascalibrationequalized-oddsstatistics

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install