Research map / Learning theory and algorithms / Fairness
Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use transitive trust estimates from shared-task answers, seeded with a small set of gold-task workers | Deep Bayesian Trust · Crowdsourcing tasks | dominant uniform strategy incentive compatibility | Helped |
| Evaluate workers by estimated accuracy rather than by their peer's ability or strategy | Deep Bayesian Trust · Crowdsourcing tasks | fairness of rewards | Helped |
| Play a heuristic strategy instead of reporting task observations truthfully | Deep Bayesian Trust · Simulated crowdsourcing | expected reward | Hurt |
| Use a permutation reporting strategy instead of truthful reporting | Deep Bayesian Trust · Simulated crowdsourcing | average reward | Hurt |
| Use Deep Bayesian Trust to incentivize effort | Crowdworkers answering natural-language-understanding tasks · Amazon Mechanical Turk preliminary study | time spent solving tasks | Helped |
| Use Deep Bayesian Trust to incentivize effort | Crowdworkers answering natural-language-understanding tasks · Amazon Mechanical Turk preliminary study | response accuracy | Helped |
| Evaluate workers using a finite number of shared tasks | Deep Bayesian Trust · Simulated crowdsourcing | reward separation by reporting strategy | No clear effect |
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