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1. A Novel Algorithm for Aggregating Crowdsourced Data

   Prihar, E.; Heffernan, Neil T. (January 2021, International Conference on Educational Data Mining)

   null (Ed.)

   Similar content has tremendous utility in classroom and online learning environments. For example, similar content can be used to combat cheating, track students’ learning over time, and model students’ latent knowledge. These different use cases for similar content all rely on different notions of similarity, which make it difficult to determine contents’ similarities. Crowdsourcing is an effective way to identify similar content in a variety of situations by providing workers with guidelines on how to identify similar content for a particular use case. However, crowdsourced opinions are rarely homogeneous and therefore must be aggregated into what is most likely the truth. This work presents the Dynamically Weighted Majority Vote method. A novel algorithm that combines aggregating workers’ crowdsourced opinions with estimating the reliability of each worker. This method was compared to the traditional majority vote method in both a simulation study and an empirical study, in which opinions on seventh grade mathematics problems’ similarity were crowdsourced from middle school math teachers and college students. In both the simulation and the empirical study the Dynamically Weighted Majority Vote method outperformed the traditional majority vote method, suggesting that this method should be used instead of majority vote in future crowdsourcing endeavors. 

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2. Crowdsourced PAC Learning under Classification Noise

   Heinecke, Shelby; Reyzin, Lev (October 2019, Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing)

   Law, Edith; Vaughan, Jennifer W (Ed.)

   In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algorithm that takes unlabeled data points as input and outputs a trained classifier. Our threestep algorithm incorporates majority voting, pure-exploration bandits, and noisy-PAC learning. We prove several guarantees on the number of tasks labeled by workers for PAC learning in this setting and show that our algorithm improves upon the baseline by reducing the total number of tasks given to workers. We demonstrate the robustness of our algorithm by exploring its application to additional realistic crowdsourcing settings. 

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3. Bayesian Decision Process for Budget-efficient Crowdsourced Clustering

   https://doi.org/10.24963/ijcai.2020/283  

   Wang, Xiaozhou; Chen, Xi; Lin, Qihang; Liu, Weidong (July 2020, International Joint Conferences on Artificial Intelligence (IJCAI))

   null (Ed.)

   The performance of clustering depends on an appropriately defined similarity between two items. When the similarity is measured based on human perception, human workers are often employed to estimate a similarity score between items in order to support clustering, leading to a procedure called crowdsourced clustering. Assuming a monetary reward is paid to a worker for each similarity score and assuming the similarities between pairs and workers' reliability have a large diversity, when the budget is limited, it is critical to wisely assign pairs of items to different workers to optimize the clustering result. We model this budget allocation problem as a Markov decision process where item pairs are dynamically assigned to workers based on the historical similarity scores they provided. We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph. We provide simulation studies and real data analysis to demonstrate the performance of the proposed method. 

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4. Dynamically monitoring crowd-worker's reliability with interval-valued labels

   https://doi.org/10.54941/ahfe1003270  

   Hu, Chenyi; Spurling, Makenzie (January 2023, AHFE International)

   Crowdsourcing has rapidly become a computing paradigm in machine learning and artificial intelligence. In crowdsourcing, multiple labels are collected from crowd-workers on an instance usually through the Internet. These labels are then aggregated as a single label to match the ground truth of the instance. Due to its open nature, human workers in crowdsourcing usually come with various levels of knowledge and socio-economic backgrounds. Effectively handling such human factors has been a focus in the study and applications of crowdsourcing. For example, Bi et al studied the impacts of worker's dedication, expertise, judgment, and task difficulty (Bi et al 2014). Qiu et al offered methods for selecting workers based on behavior prediction (Qiu et al 2016). Barbosa and Chen suggested rehumanizing crowdsourcing to deal with human biases (Barbosa 2019). Checco et al studied adversarial attacks on crowdsourcing for quality control (Checco et al 2020). There are many more related works available in literature. In contrast to commonly used binary-valued labels, interval-valued labels (IVLs) have been introduced very recently (Hu et al 2021). Applying statistical and probabilistic properties of interval-valued datasets, Spurling et al quantitatively defined worker's reliability in four measures: correctness, confidence, stability, and predictability (Spurling et al 2021). Calculating these measures, except correctness, does not require the ground truth of each instance but only worker’s IVLs. Applying these quantified reliability measures, people have significantly improved the overall quality of crowdsourcing (Spurling et al 2022). However, in real world applications, the reliability of a worker may vary from time to time rather than a constant. It is necessary to monitor worker’s reliability dynamically. Because a worker j labels instances sequentially, we treat j’s IVLs as an interval-valued time series in our approach. Assuming j’s reliability relies on the IVLs within a time window only, we calculate j’s reliability measures with the IVLs in the current time window. Moving the time window forward with our proposed practical strategies, we can monitor j’s reliability dynamically. Furthermore, the four reliability measures derived from IVLs are time varying too. With regression analysis, we can separate each reliability measure as an explainable trend and possible errors. To validate our approaches, we use four real world benchmark datasets in our computational experiments. Here are the main findings. The reliability weighted interval majority voting (WIMV) and weighted preferred matching probability (WPMP) schemes consistently overperform the base schemes in terms of much higher accuracy, precision, recall, and F1-score. Note: the base schemes are majority voting (MV), interval majority voting (IMV), and preferred matching probability (PMP). Through monitoring worker’s reliability, our computational experiments have successfully identified possible attackers. By removing identified attackers, we have ensured the quality. We have also examined the impact of window size selection. It is necessary to monitor worker’s reliability dynamically, and our computational results evident the potential success of our approaches.This work is partially supported by the US National Science Foundation through the grant award NSF/OIA-1946391. 

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5. A Bandit you can Trust.

   Prihar, E. (June 2023, Proceedings of The 31st ACM Conference On User Modeling, Adaptation And Personalization)

   This work proposes Dynamic Linear Epsilon-Greedy, a novel con- textual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning ap- proaches have trade-offs between empirical investigation and max- imal impact on users. Our algorithm seeks to balance these objec- tives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSIST- ments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to bal- ance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. 

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