  High performance machine learning models have become highly dependent on the
availability of large quantity and quality of training data. To achieve this,
various central agencies such as the government have suggested for different
data providers to pool their data together to learn a unified predictive model,
which performs better. However, these providers are usually profit-driven and
would only agree to participate inthe data sharing process if the process is
deemed both profitable and fair for themselves. Due to the lack of existing
literature, it is unclear whether a fair and stable outcome is possible in such
data sharing processes. Hence, we wish to investigate the outcomes surrounding
these scenarios and study if data providers would even agree to collaborate in
the first place. Tapping on cooperative game concepts in Game Theory, we
introduce the data sharing process between a group of agents as a new class of
cooperative games with modified definition of stability and fairness. Using
these new definitions, we then theoretically study the optimal and suboptimal
outcomes of such data sharing processes and their sensitivity to
perturbation.Through experiments, we present intuitive insights regarding
theoretical results analysed in this paper and discuss various ways in which
data can be valued reasonably.
