# Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd

arXiv: https://arxiv.org/abs/1804.05560 · HTML: https://mlsanity.com/paper/1804.05560/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Data Pricing in Machine Learning Pipelines](https://mlsanity.com/paper/2108.07915/) (2021)
- [SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing](https://mlsanity.com/paper/2509.11265/) (2025)
- [Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms](https://mlsanity.com/paper/2402.13567/) (2024)
- [Better Peer Grading through Bayesian Inference](https://mlsanity.com/paper/2209.01242/) (2022)
- [Data-Driven Evaluation of Training Action Space for Reinforcement Learning](https://mlsanity.com/paper/2204.03840/) (2022)

## Abstract (arXiv)

An important class of game-theoretic incentive mechanisms for eliciting effort from a crowd are the peer based mechanisms, in which workers are paid by matching their answers with one another. The other classic mechanism is to have the workers solve some gold standard tasks and pay them according to their accuracy on gold tasks. This mechanism ensures stronger incentive compatibility than the peer based mechanisms but assigning gold tasks to all workers becomes inefficient at large scale. We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers' accuracy. We show that the resulting mechanism ensures a dominant notion of incentive compatibility and fairness.