Before revision:  This places fair-PAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
After revision:  This places FPAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .