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

Strategyproof Learning: Building Trustworthy User-Generated Datasets