no code implementations • 19 May 2022 • Ludwig Bothmann, Kristina Peters, Bernd Bischl
A growing body of literature in fairness-aware ML (fairML) aspires to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods that ensure that trained ML models achieve low values in those metrics.