Fairness Testing

Fairness testing evaluates whether an AI system produces systematically different outcomes for different groups of people in ways that are unjustified, harmful, or legally impermissible. It involves selecting appropriate fairness metrics (which vary by system, context, and legal framework) and measuring the system's outputs across demographic groups and protected characteristics. 

There is no single definition of fairness in AI: different metrics can produce conflicting results, and the right metric depends on the values and objectives of the organization deploying the system. At Eticas.ai, fairness testing is conducted using the system's actual outputs in the environment where it operates, with the populations it actually serves. Our open-source library provides demographic benchmarking, disparate impact measurement, model fairness metrics, and performance analysis disaggregated by group across all stages of the AI lifecycle.