Bias detection identifies systematic imbalances in training data that could lead to unfair or discriminatory model behavior. The framework evaluates datasets along multiple bias dimensions including demographic representation, linguistic diversity, geographic coverage, and topic balance.
Demographic analysis measures the distribution of content related to different identity groups across protected attributes. The analysis uses both explicit mentions and contextual inference to build a comprehensive representation profile.
Linguistic bias detection identifies stereotypical associations, sentiment disparities, and framing differences in how different groups or topics are described. The detector uses calibrated reference distributions derived from balanced corpora.
Mitigation recommendations suggest specific actions to address detected biases, such as targeted data collection, reweighting strategies, or augmentation with counter-stereotypical examples. Each recommendation includes an estimated effort and expected impact.
Audit reports are formatted for both technical teams and compliance reviewers, with executive summaries, detailed statistical analyses, and appendices documenting methodology.