ABSTRACT
In the world, the popularity of artificial intelligence (AI) enlarged the researcher trying to incorporate human behavior into the machine. Due to this, it led to too many problems that machines cannot solve alone. So, the researcher thinks that machines and humans can act together, which led to the new field called crowdsourcing. Crowdsourcing is used to address problems that are very hard to solve by the machine independently and require human intelligence. However, the openness of crowdsourcing increased the requirements of the crowd (called workers), which led to creating the low quality of data and redundant data due to the availability of low-quality workers. To solve this problem, many redundancy-based algorithms can be used in which they assign each task to the worker to find the correctness of the answer called truth, and this fundamental problem is known as truth inference, which decides how effectively they infer the truth. In this chapter, we compare some of the existing truth inference algorithms and make a comPara_1tive study of the algorithms in real-time datasets.
