La incompatibilidad de métricas de equidad es el principio de que diferentes definiciones formales de equidad pueden entrar en conflicto y no siempre pueden satisfacerse al mismo tiempo. Significa que la equidad debe seleccionarse y justificarse en contexto, según el caso de uso, los grupos afectados, los riesgos y los daños abordados.
The idea that some notions of fairness are mutually incompatible and cannot be satisfied simultaneously. As a result, there is no single universal metric for quantifying fairness that can be applied to all ML problems. While this may seem discouraging, incompatibility of fairness metrics doesn't imply that fairness efforts are fruitless. Instead, it suggests that fairness must be defined contextually for a given ML problem, with the goal of preventing harms specific to its use cases. See "On the (im)possibility of fairness" for a more detailed discussion of the incompatibility of fairness metrics.