version: prml/0.1
claim_id: 01997a2c-0000-7000-8000-000000000001
created_at: '2026-08-25T08:22:01Z'
metric: impact_ratio
metric_args:
  protected_attribute: sex
  protected_group: female
  reference_group: male
  group_definition: 'attribute 9 (personal status and sex): A92 is female; A91, A93, A94 are male'
  favourable_outcome: predicted class 1 (good credit)
  split: stratified 70/30 train/test at the manifest seed
  features: all 20 attributes; categorical one-hot encoded; numeric standardised
  model_config: scikit-learn LogisticRegression, L2 penalty, C=1.0, max_iter=2000
  definition: female selection rate divided by male selection rate on the held-out test split
comparator: '>='
threshold: 0.8
dataset:
  id: uci-statlog-german-credit
  hash: b21f3d81db8071257d5ff1deaeba1fd4303b62712e6fcc9715c7a86202cb5871
  uri: https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data
seed: 42
producer:
  id: worked-example.falsify
model:
  id: sklearn-logistic-regression-l2-C1.0
notes: Worked example produced by Falsify OU; not a client engagement. 0.80 is the EEOC four-fifths benchmark, a rule of thumb applied by bias-audit practice and not a legal standard; no statute fixes it. It is used because it is the convention an assurance reader recognises. The outcome was not known to the author when this manifest was sealed.