in Game Theory. Our research focus in this paper lies strongly in the theoretical representation of a multi-party machine learning process as a new class of cooperative game with new definitions of stability and fairness. After which, we analyse various optimal and suboptimal outcomes in such games and show how some of these outcomes can be derived. These outcomes provide insights into how data providers behave in a multi-party machine learning process and allow us to theoretically investigate if the collaboration will be successful and give desirable results.\
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In Section 3, we provide a brief overview of cooperative games in Game Theory and show that conventional cooperative games are unable to account for some unique subtleties present in data sharing and other similar processes. In Section 4 and 5, we modify conventional assumptions in cooperative games to account for this and introduce a new class of cooperative game with the production of non-rivalrous goods; we also redefine the concept of stability and fairness in such games by tapping on the conventional concept of *Shapley value* in Game Theory. In Section 6, we introduce a computationally efficient way to check if an outcome of a modified game is stable and 