Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large
groups of individuals toward solving problems. Common problems approached with …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

Online crowdsourcing provides a scalable and inexpensive means to collect knowledge (eg
labels) about various types of data items (eg text, audio, video). However, it is also known to …

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 …

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

Peer grading, allowing students/peers to evaluate others' assignments, offers a promising
solution for scaling evaluation and learning to large-scale educational systems. A key …

A key problem in crowdsourcing is the aggregation of judgments of proportions. For
example, workers might be presented with a news article or an image, and be asked to …

Peer grading is a natural crowdsourcing application, where dispersed students/peers
resources are collected to evaluate others' assignments. Peer grading also offers a …

Aggregation functions are widely used to fuse information from different sources in a unique
value. In many cases, the aggregated information is related to some experimental measure …

Artificial intelligence (AI) demonstrates superiority over humans in many applications, such
as image processing, speech recognition, and decision-making. AI systems are more …