With the online-everything transformation accelerated by a global Covid-19 pandemic, we
may finally find ourselves on the verge of the next potentially paradigm-shifting step after the …

The rawly collected training data often comes with separate noisy labels collected from
multiple imperfect annotators (eg, via crowdsourcing). A typical way of using these separate …

This paper explores the impact of value similarity between humans and AI on human
reliance in the context of AI-assisted ethical decision-making. Using kidney allocation as a …

Many crowdsourced NLP datasets contain systematic gaps and biases that are identified
only after data collection is complete. Identifying these issues from early data samples …

Collecting large-scale human-annotated datasets via crowdsourcing to train and improve
automated models is a prominent human-in-the-loop approach to integrate human and …

Crowdsourcing is widely used to solicit judgement from people in diverse applications
ranging from evaluating information quality to rating gig worker performance. To encourage …

Implications The circulation of poor-quality information on the internet is a well-known
problem with huge costs for our societies in the short and long term (Bruns et al., 2021; …

We study the environment design problem for biased decision makers. In an environment
design problem, an informed principal aims to update the decision making environment to …

Earlier research has shown the promise of enabling worker interactions in crowdwork to
mitigate worker biases and improve the quality of crowdwork. In this study, we focus on one …

Training workers within a task is one way of enabling novice workers, who may lack domain
knowledge or experience, to work on complex crowdsourcing tasks. Based on goal setting …