Can I read Strategyproof Learning: Building Trustworthy User-Generated Datasets on EtoBox?

Strategyproof Learning: Building Trustworthy User-Generated Datasets by Farhadkhani, Sadegh; Guerraoui, Rachid; Hoang, Lê-Nguyên is a scholarly article available to read on EtoBox.

What is Strategyproof Learning: Building Trustworthy User-Generated Datasets about?

We prove in this paper that, perhaps surprisingly, incentivizing data misreporting is not a fatality. By leveraging a careful design of the loss function, we propose Licchavi, a global and personalized learning framework with provable strategyproofness guarantees. Essentially, we prove that no user can gain much by replying to Licchavi's queries with answers that deviate from their true preferences. Interestingly, Licchavi also promotes the desirable "one person, one unit-force vote" fairness principle. Furthermore, our empirical evaluation of its performance showcases Licchavi's real-world applicability. We believe that our results are critical for the safety of any learning scheme that leverages user-generated data.

Author

Farhadkhani, Sadegh; Guerraoui, Rachid; Hoang, Lê-Nguyên

Published

2021

Language

EN