FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair

We present FAIR-SIGHT, a post-hoc framework that enforces statistical fairness in computer vision models without retraining or access to internal parameters. The method computes a fairness-aware non-conformity score combining prediction error and demographic disparity and uses conformal prediction to calibrate a threshold that guarantees a user-specified violation rate under finite-sample, distribution-free conditions. Inputs exceeding this threshold are automatically repaired through lightweight adjustments (such as logit shifts for classification or confidence scaling for detection) to reduce group disparities while preserving accuracy. Experiments on CelebA, UTKFace, GeoDE, and COCO show over 30% reductions in DPD/EOD and detection gaps with negligible utility loss and only ∼0.5ms/img overhead, establishing FAIR-SIGHT as a practical, scalable solution for bias mitigation in black-box vision systems.