Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses a fairness criterion originating in the field of educational and psychological testing that has recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate how adherence to the criterion may lead to considerable disparate impact when recidivism prevalence differs across groups.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
An audit of one million Korean synthetic personas shows marginal demographic alignment does not preserve joint distributions, with three specific mismatches identified via a new Independence-Assumption Footprint method.
Facial recognition enacts computational epistemicide by progressively reducing faces to standardized numerical vectors, rendering reformist ethical AI insufficient and requiring abolition of vectorized identity as a basis for rights.
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.