The most popular methods for measuring importance of the variables in a black
box prediction algorithm make use of synthetic inputs that combine predictor
variables from multiple subjects. These inputs can be unlikely, physically
impossible, or even logically impossible. As a result, the predictions for such
cases can be based on data very unlike any the black box was trained on. We
think that users cannot trust an explanation of the decision of a prediction
algorithm when the explanation uses such values. Instead we advocate a method
called Cohort Shapley that is grounded in economic game theory and unlike most
other game theoretic methods, it uses only actually observed data to quantify
variable importance. Cohort Shapley works by narrowing the cohort of subjects
judged to be similar to a target subject on one or more features. A feature is
important if using it to narrow the cohort makes a large difference to the
cohort mean. We illustrate it on an algorithmic fairness problem where it is
essential to attribute importance to protected variables that the model was not
trained on. For every subject and every predictor variable, we can compute the
importance of that predictor to the subject's predicted response or to their
actual response. These values can be aggregated, for example over all Black
subjects, and we propose a Bayesian bootstrap to quantify uncertainty in both
individual and aggregate Shapley values.

使用合作谢普利方法（Cohort Shapley）来验证黑盒预测算法的重要变量，并通过经济博弈理论来量化变量的重要性。该方法可以用于算法公平性问题，以确定受保护变量的重要性，并通过贝叶斯自助法估计 Shapley 值的不确定性。