1. Trust-Aware Optimal Crowdsourcing With Budget Constraint Xiangyang Liu 1, He He 2, and John S. Baras 1 1 Institute for Systems Research and Department of Electrical and Computer Engineering University of Maryland, College Park, MD 2 Deptment of Computer Science, University of Maryland, College Park, MD

2. Motivation Requester has budget constraint. Workers on AMT platform have varying reliability. Some are even malicious. Each task incurs certain amount of cost. Difficult tasks are more expensive and easy tasks are cheaper. Workers with higher reliability should expect to receive higher pay and workers with lower reliability are cheaper to be recruited to answer questions. Goal: optimally assign tasks to workers with varying trust and reliability under budget constraint.

3. Problem Setting Crowdsourcing Assignment Engine Malicious workers More reliable workers Pure experts Amazon Turkers Trust Evaluation True Label Inference Objective: minimize estimation error Task distributed to Turkers

4. Problem Formulation Crowdsourcing Assignment Engine Objective: minimize estimation error Task distributed to Turkers depends on the choice of estimation algorithm. is the truth value for question i. w are estimated trust values of workers given by an independent component introduced in the previous subproblem. It is assumed to be fixed and serves as input to the assignment engine.

5. Applying Probabilistic Approximation Correction (PAC) from learning theory, we relax the previously nondeterministic optimization problem into a convex optimization problem: Proof: Let the right hand side equals Trust-Aware Budget Allocation

6. Proof continued: With probability, the following holds: We express as: If, question i is always estimated correctly. Otherwise we get the wrong answer with probability Trust-Aware Budget Allocation

7. Proof continued: Therefore, we obtain the upper bound on the error rate which we are going to minimize The last inequality holds since w’s takes values from [0,1]. Therefore, we relaxed the optimization problem to minimizing the new upper bound: Trust-Aware Budget Allocation

8. Applying Probabilistic Approximation Correction (PAC) from learning theory, we get a relaxed problem: Intuition in the solution: when budget is not sufficient, assign budget to the most efficient workers. The most efficient worker is defined to be a worker that has highest reliability (squared)-cost ratio. Analytical solution

9. Trust-Aware Budget Allocation With Penalty Intuition: when budget is high, we want to allocate budget to expensive workers (more trustworthy) instead of just efficient workers. Therefore, the taste of uniform strategy enforced by the penalty term serves exactly this purpose.

10. Theoretical Guarantees We provide the upper bound on the error probability rate of the trust-aware budget allocation scheme: given budget B, i.e., The error bound above has the following characteristics: decreases exponential with budget B. decreases when cost is lower. decreases when workers are more reliable.

11. Theoretical Guarantees Proof Assume weight majority vote, the labels contributed by workers are aggregated by Hoeffding concentration bound gives us: Plugging the optimal solution we obtain the bound straightforwardly.

12. Theoretical Guarantees If, with probability at least, the total error probability satisfies:

13. Experiment Benchmarks UA: the algorithm tends to allocate the same number of people to answer a question from each available crowd. If the budget is not used up, for each question, it randomly chooses an expert from the set of crowds. CQSA: for each question, the algorithm only chooses people from the most trustworthy crowd to assign If budget is not consumed, it iterates the question set again and randomly chooses an expert from the set of crowds for each question. CA: the algorithm only chooses the cheapest crowd (the least trustworthy crowd) for questions according to

14. Experiment Results TAAP performs the best out of all benchmarks across the span of budget. When budget is small (<200), the improvement of TAA and TAAP over CQSA and UA is up by 30%. TAA performs poorly when budget is high due to the floor function during assignment and the sparsity feature in the optimal solution in the relaxed problem (only workers from the most efficient group are chosen). This is fixed by TAAP. TAA: Trust-aware allocation. TAAP: Trust-aware allocation with penalty.

15. Experiment Results In this experiment, we do not assume the trust values of workers can be perfectly estimated. We add Gaussian noise on the true trust values of workers. We do experiments with varying level of noises (increasing means of Gaussian noise). TAAP is affected to some extend when the noise goes high. However, when budget is high, the allocation scheme is very robust against all levels of noises.

16. Conclusions We formalize the problem of trust-aware task allocation in crowdsourcing and provide a principled way to solve it. We model the workers’ trustworthiness as reliability and the cost depends on both workers’ trusts and questions’ difficulty. Our method is flexible in that you can plugin more complicated aggregation method other than weighted majority vote. We provide theoretical guarantee for our trust-aware allocation scheme.

17. Future Work The theoretical guarantee for the trust-aware algorithm when the weight (trust) can not be perfectly estimated has not been addressed yet. It would be interesting to investigate this. Now the trust estimate is assumed to be fixed and given by another component. We will consider the case where trust is dynamically updated and crowdsourcing assignment is done online instead of offline.

18. Thank you