Towards Fair Truth Discovery from Biased Crowdsourced Answers
Yanying Li: Stevens Institute of Technology; Haipei Sun: Stevens Institute of Technology; Wendy Hui Wang: Stevens Institute of Technology
Crowdsourcing systems have gained considerable interest and adoption in recent years. One important research problem for crowdsourcing systems is truth discovery, which aims to aggregate noisy answers contributed by the workers to obtain the correct answer (truth) of each task. However, since the collected answers are highly prone to the workers’ biases, aggregating these biased answers without proper treatment will unavoidably lead to discriminatory truth discovery results for particular race, gender and political groups. To address this challenge, in this paper, first, we define a new fairness notion named θ-disparity for truth discovery. Intuitively, θ-disparity bounds the difference in the probabilities that the truth of both protected and unprotected groups being predicted to be positive. Second, we design three fairness enhancing methods, namely Pre-TD, FairTD, and Post-TD, for truth discovery. Pre-TD is a pre-processing method that removes the bias in workers’ answers before truth discovery. FairTD is an in-processing method that incorporates fairness into the truth discovery process. And Post-TD is a post-processing method that applies additional treatment on the discovered truth to make it satisfy θ-disparity. We perform an extensive set of experiments on both synthetic and real-world crowdsourcing datasets. Our results demonstrate that among the three fairness enhancing methods, FairTD produces the best accuracy with θ-disparity. In some settings, the accuracy of FairTD is even better than truth discovery without fairness, as it removes some low-quality answers as side effects.
How can we assist you?
We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!
Please enter the word you see in the image below: