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Austin Chen

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2 papers

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AAAI Conference 2023 Short Paper

ACCD: An Adaptive Clustering-Based Collusion Detector in Crowdsourcing (Student Abstract)

Ruoyu Xu
Gaoxiang Li
Wei Jin
Austin Chen
Victor S. Sheng

Crowdsourcing is a popular method for crowd workers to collaborate on tasks. However, workers coordinate and share answers during the crowdsourcing process. The term for this is "collusion". Copies from others and repeated submissions are detrimental to the quality of the assignments. The majority of the existing research on collusion detection is limited to ground truth problems (e.g., labeling tasks) and requires a predetermined threshold to be established in advance. In this paper, we aim to detect collusion behavior of workers in an adaptive way, and propose an Adaptive Clustering Based Collusion Detection approach (ACCD) for a broad range of task types and data types solved via crowdsourcing (e.g., continuous rating with or without distributions). Extensive experiments on both real-world and synthetic datasets show the superiority of ACCD over state-of-the-art approaches.

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AAAI Conference 2023 Short Paper

Towards Fair and Selectively Privacy-Preserving Models Using Negative Multi-Task Learning (Student Abstract)

Liyuan Gao
Huixin Zhan
Austin Chen
Victor S. Sheng

Deep learning models have shown great performances in natural language processing tasks. While much attention has been paid to improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In order to tackle these problems, we protect individuals' sensitive information and mitigate gender bias simultaneously. First, we propose a selective privacy-preserving method that only obscures individuals' sensitive information. Then we propose a negative multi-task learning framework to mitigate the gender bias which contains a main task and a gender prediction task. We analyze two existing word embeddings and evaluate them on sentiment analysis and a medical text classification task. Our experimental results show that our negative multi-task learning framework can mitigate the gender bias while keeping models’ utility.

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