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A learning algorithm $A$ trained on a dataset $D$ is revealed to have poorperformance on some subpopulation at test time. Where should the responsibilityfor this lay? It can be argued that the data is responsible, if for exampletraining $A$ on a more representative dataset $D'$ would have improved theperformance. But it can similarly be argued that $A$ itself is at fault, iftraining a different variant $A'$ on the same dataset $D$ would have improvedperformance. As ML becomes widespread and such failure cases more common, thesetypes of questions are proving to be far from hypothetical. With thismotivation in mind, in this work we provide a rigorous formulation of the jointcredit assignment problem between a learning algorithm $A$ and a dataset $D$.We propose Extended Shapley as a principled framework for this problem, andexperiment empirically with how it can be used to address questions of MLaccountability.

Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data