[null,null,["最后更新时间 (UTC):2024-08-13。"],[[["Equality of opportunity in machine learning focuses on ensuring that qualified individuals have an equal chance of being accepted, regardless of their demographic group."],["It's achieved when the acceptance rates for qualified individuals are the same across different demographic groups, as illustrated by the example with a 40% acceptance rate for qualified candidates in both the majority and minority groups."],["While it promotes fairness in specific scenarios, equality of opportunity has limitations, such as its dependence on a clear preferred label and potential challenges in situations lacking demographic data."],["Unlike demographic parity which focuses on overall acceptance rates, equality of opportunity concentrates on the acceptance rates within the qualified subset of each group."],["It's possible for a model to satisfy both demographic parity and equality of opportunity simultaneously, under specific conditions where positive prediction rates and true positive rates are balanced across groups."]]],[]]