A new framework addresses data bias in machine learning by incorporating coverage constraints to ensure sufficient representation of intersectional subgroups. It trades small bias errors for greater data efficiency and formulates bias mitigation as an integer linear program, characterizing the price of fairness as a function of fairness tolerance to guide data governance and legal compliance.