A Marketplace for Data: An Algorithmic Solution

By Anish Agarwal, Munther A. Dahleh, Tuhin Sarkar. 2018

306 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Marketing

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Citations in the corpus

Towards Standardization of Data Licenses: The Montreal Data License (2019, neutral): “When combined with recent works such as the two-sided markets conceptualized by Agarwal, et al.(Agarwal et al., 2018), one can construct the foundation of markets for data that can help foster transparency, algorithmic fairness and fair market dynamics.”
Too Much Data: Prices and Inefficiencies in Data Markets (2019, unknown): “and Skrzypacz [2016], Bergemann et al. [2018], and Eliaz et al. [2019] consider either static or dynamic mechanisms for selling data, Ghosh and Roth [2015] uses differential privacy framework of Dwork et al.”
Forecasting: theory and practice (2020, background): “An alternative approach is to design a market (or auction) mechanism for time series or forecasting data where the data owners are willing to sell their private (or confidential) data in exchange for an economic compensation (Agarwal et al., 2019).”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “To accomplish this, researchers estimate the Shapley value of the data points, which assigns weights to each data point based on its contribution [3, 79].”