Controlled synthetic fairness dataset for evaluating adversarial governance response (IGRA)

A controlled synthetic tabular dataset (4000 instances) constructed to support fairness measurement under data-poisoning attack. Each record has two informative features drawn independently of group membership, six uninformative noise features, a binary protected-group attribute at a 65/35 split, and a binary label assigned by a median threshold on a latent score. The protected attribute is generated independently of both the predictive features and the label-generating process, so baseline group parity holds by construction. Any post-attack disparity is therefore attributable to the induced poisoning intervention and not to pre-existing imbalance in an uncontrolled source. The label split is 50/50 by construction. This file is the seed-42 instance. The generator that produces all eight seed instances used in the associated article, together with the full experimental code, is available at https://github.com/Maxwellson/igra-adversarial-ml-governance