Unverified50% confidenceFactExact time
The fairness standard ProPublica used is "group balance in predictive error rates," while the company that developed COMPAS countered that the system is calibrated on "predictive accuracy" for both groups, i.e., the actual recidivism rate corresponding to a high risk score is roughly the same across races.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFairness is measured by Equalized Odds, a metric that requires the model to maintain consistent true positive rates and false positive rates across different demographic groups.75% similarUnverifiedSystems with the same overall accuracy may produce markedly different error distributions across two population groups, for example frequently wrongly rejecting applicants in group A who would have repaid on time, and more often wrongly approving applicants in group B who ultimately defaulted.73% similarUnverifiedAverage accuracy masks distributional skew; a judge that gets all easy cases right and is overconfident on hard cases can post a high average score while failing at critical points.72% similarVerifiedWhen samples are severely imbalanced, Accuracy becomes misleading—a model that predicts everyone as healthy can still achieve an accuracy of over 99%.72% similarUnverifiedIn class-imbalance scenarios, a model can achieve seemingly impressive accuracy by always predicting the majority class, a phenomenon known as the Accuracy Paradox.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1060053API
curl https://kongchang.com/api/v1/knowledge/claims/1060053MCP
get_claim(id=1060053)