Incentivizing Time-Aware Fairness in Data Sharing

Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Arun Verma, Zhaoxuan Wu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Keywords: Cooperative Game Theory, Incentives, Data Sharing

TL;DR: A novel time-aware data sharing framework that fairly decides and distributes model rewards to participating parties based on their value of data and time of participation to satisfy incentives.

Abstract:

In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance.
However, as the parties incur data collection costs, they are only willing to do so when guaranteed incentives, such as fairness and individual rationality.
Existing frameworks assume that all parties join the collaboration simultaneously, which does not hold in many real-world scenarios.
Due to the long processing time for data cleaning, difficulty in overcoming legal barriers, or unawareness, the parties may join the collaboration at different times.
In this work, we propose the following perspective:
As a party who joins earlier incurs higher risk and encourages the contribution from other wait-and-see parties, that party should receive a reward of higher value for sharing data earlier.
To this end, we propose a fair and time-aware data sharing framework, including novel time-aware incentives.
We develop new methods for deciding reward values to satisfy these incentives.
We further illustrate how to generate model rewards that realize the reward values and empirically demonstrate the properties of our methods on synthetic and real-world datasets.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 17013