Our audit detects Selection Bias (unrepresentative training data), Algorithmic Bias (flawed logic or weighting), and Proxy Bias (where non-sensitive data, like zip codes, inadvertently reflects protected characteristics like race or income).

We map your AI systems against the specific “High-Risk” requirements of the EU AI Act. This includes evaluating your data governance, technical documentation, and human oversight protocols to ensure you meet mandatory transparency and fairness standards.

Yes. We use Counterfactual Testing and Input-Output Analysis to observe how a model reacts to different demographic prompts. This allows us to quantify bias and disparate impact even without direct access to the underlying proprietary code.

You receive a Bias Scorecard and a Remediation Roadmap. This includes a quantitative breakdown of fairness metrics, a risk-level classification for your use case, and step-by-step technical instructions on how to retrain or re-weight your model to eliminate identified biases.