A Marketplace for Data: An Algorithmic Solution

Abstract

In this work, we aim to design 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 sellers 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: (i) it is freely replicable; (ii) its value is inherently combinatorial due to correlation with signal in other data; (iii) prediction tasks and the value of accuracy vary widely; (iv) 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 the key associated challenges; (ii) construct algorithms for such a market to function and analyze how they meet the challenges defined. We highlight two technical contributions: (i) a new notion of 'fairness' required for cooperative games with freely replicable goods; (ii) a truthful, zero regret mechanism to auction a class of combinatorial goods based on utilizing Myerson's payment function and the Multiplicative Weights algorithm. These might be of independent interest.

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• Scikit-learn: Machine Learning in Python
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• Near-optimal Nonmyopic Value of Information in Graphical Models
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Cited by in corpus (6)

• Towards Standardization of Data Licenses: The Montreal Data License
• Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data
• DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
• Replication-Robust Payoff-Allocation for Machine Learning Data Markets
• Zorro: A Model Agnostic System to Price Consumer Data
• Bayesian Regression Markets