Before revision:  Building upon these concepts, we define fair-PAC  learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any  malfare concept.
After revision:  Building upon these concepts, we define fair-PAC learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any (axiomatically justified) malfare concept.