With inputs from human crowds, usually through
the Internet, crowdsourcing has become a promising methodology
in AI and machine learning for applications that require human
knowledge. Researchers have recently proposed interval-valued
labels (IVLs), instead of commonly used binary-valued ones,
to manage uncertainty in crowdsourcing [19]. However, that
work has not yet taken the crowd worker’s reliability into
consideration. Crowd workers usually come with various social
and economic backgrounds, and have different levels of reliability.
To further improve the overall quality of crowdsourcing with
IVLs, this work presents practical methods that quantitatively
estimate worker’s reliability in terms of his/her correctness,
confidence, stability, and predictability from his/her IVLs. With
worker’s reliability, this paper proposes two learning schemes:
weighted interval majority voting (WIMV) and weighted preferred
matching probability (WPMP). Computational experiments
on sample datasets demonstrate that both WIMV and
WPMP can significantly improve learning results in terms of
higher precision, accuracy, and F1-score than other methods.
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Dynamically monitoring crowd-worker's reliability with interval-valued labels
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.
more » « less- Award ID(s):
- 1946391
- NSF-PAR ID:
- 10497403
- Publisher / Repository:
- AHFE International
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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