Crowdsourcing provides a scalable and efficient way to construct labeled datasets for
training machine learning systems. However, creating comprehensive label guidelines for …

While artificial intelligence (AI) holds promise for supporting healthcare providers and
improving the accuracy of medical diagnoses, a lack of transparency in the composition of …

A standard approach to exploring a domain is to conduct a literature review. However,
conducting a literature review in a new domain presents unique challenges. Whereas in an …

The internet is empowering the rise of crowd work, gig work, and other forms of on-demand
labor. A large and growing body of scholarship has attempted to predict the socio-technical …

We present hybrid crowd-machine learning classifiers: classification models that start with a
written description of a learning goal, use the crowd to suggest predictive features and label …

AI systems can fail to learn important behaviors, leading to real-world issues like safety
concerns and biases. Discovering these systematic failures often requires significant …

A growing number of large collaborative idea generation platforms promise that by
generating ideas together, people can create better ideas than any would have alone. But …

Website privacy policies are often long and difficult to understand. While research shows
that Internet users care about their privacy, they do not have time to understand the policies …

In the past half-decade, Amazon Mechanical Turk has radically changed the way many
scholars do research. The availability of a massive, distributed, anonymous crowd of …

Crowdsourcing systems accomplish large tasks with scale and speed by breaking work
down into independent parts. However, many types of complex creative work, such as fiction …