Algorithm aversion occurs when humans are reluctant to use algorithms despite their
superior performance. Studies show that giving users outcome control by providing agency …

Crowdsourcing is being increasingly adopted as a platform to run studies with human
subjects. Running a crowdsourcing experiment involves several choices and strategies to …

Recently, the misinformation problem has been addressed with a crowdsourcing-based
approach: to assess the truthfulness of a statement, instead of relying on a few experts, a …

The rapid entry of machine learning approaches in our daily activities and high-stakes
domains demands transparency and scrutiny of their fairness and reliability. To help gauge …

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet
they are often only mentioned in passing in the scholarly literature. A lack of details …

Crowdsourcing has been used in computational pathology to generate cell and cell nuclei
annotations for machine learning. Herein, we broaden its scope to the previously unsolved …

Data attained through crowdsourcing have an essential role in the development of computer
vision algorithms. Crowdsourced data might include reporting biases, since crowdworkers …

Abstract Knowledge graphs are important in human-centered AI because of their ability to
reduce the need for large labelled machine-learning datasets, facilitate transfer learning …

Factors such as instructions, payment schemes, platform demographics, along with
strategies for mapping studies into crowdsourcing environments, play an important role in …

This paper explores and offers guidance on a specific and relevant problem in task design
for crowdsourcing: how to formulate a complex question used to classify a set of items. In …