  Attribution scores can be applied in data management to quantify the
contribution of individual items to conclusions from the data, as part of the
explanation of what led to these conclusions. In Artificial Intelligence,
Machine Learning, and Data Management, some of the common scores are
deployments of the Shapley value, a formula for profit sharing in cooperative
game theory. Since its invention in the 1950s, the Shapley value has been used
for contribution measurement in many fields, from economics to law, with its
latest researched applications in modern machine learning. Recent studies
investigated the application of the Shapley value to database management. This
article gives an overview of recent results on the computational complexity of
the Shapley value for measuring the contribution of tuples to query answers and
to the extent of inconsistency with respect to integrity constraints. More
specifically, the article highlights lower and upper bounds on the complexity
of calculating the Shapley value, either exactly or approximately, as well as
solutions for realizing the calculation in practice.
