  A learning algorithm $A$ trained on a dataset $D$ is revealed to have poor
performance on some subpopulation at test time. Where should the responsibility
for this lay? It can be argued that the data is responsible, if for example
training $A$ on a more representative dataset $D'$ would have improved the
performance. But it can similarly be argued that $A$ itself is at fault, if
training a different variant $A'$ on the same dataset $D$ would have improved
performance. As ML becomes widespread and such failure cases more common, these
types of questions are proving to be far from hypothetical. With this
motivation in mind, in this work we provide a rigorous formulation of the joint
credit assignment problem between a learning algorithm $A$ and a dataset $D$.
We propose Extended Shapley as a principled framework for this problem, and
experiment empirically with how it can be used to address questions of ML
accountability.
