A Marketplace for Data: An Algorithmic Solution
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In this work, we aim to create a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetization of data and pre-trained models is an essential focus of industry today, there does not exist a market mechanism to price training data and match buyers to vendors while still addressing the associated (computational and other) complexity. The challenge in creating such a market stems from the very nature of data as an asset: it is freely replicable; its value is inherently combinatorial due to correlation with signal in other data; prediction tasks and the value of accuracy vary widely; usefulness of training data is difficult to verify a priori without first applying it to a prediction task. As our main contributions we: (i) propose a mathematical model for a two-sided data market and formally define key challenges; (ii) construct algorithms for such a market to function and rigorously prove how they meet the challenges defined. We highlight two technical contributions: (i) a remarkable link between Myerson's payment function arising in mechanism design and the Lovasz extension arising in submodular optimization; (ii) a novel notion of "fairness" required for cooperative games with freely replicable goods. These might be of independent interest.
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