Crowdsourcing has been used to produce impactful and large-scale datasets for Machine Learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in the early 2000s, the AI community has been studying its computational, system design, and data-centric aspects from various angles at such workshops as CSS, CrowdML, DCAI, and HILL.

This workshop focuses on improving the welfare of the invisible labeling crowd force behind the AI, i.e., the crowdworkers.

# Focus Areas

We welcome studies that focus on the following (non-exhaustive) list of topics:

- developing and enhancing crowdworker-centric tools that offer task matching, requester assessment, and instruction validation, among other topics;
— active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines;
- designing and applying such techniques in various domains, including e-commerce and medicine.

# Guidelines and Submission

Find format guidelines and submit a paper!

https://easychair.org/cfp/wsdm2023crowdscience

Submissions due: January 5, 2023
Notifications due: February 1, 2023
Workshop date: March 3, 2023

# Paper Presentation

At least one author of each accepted paper must register for the main conference, WSDM 2023, to present their work.

# Invited Speakers

Ujwal Gadiraju, Delft University of Technology
Djellel Difallah, NYU Abu Dhabi

# Organizers

- Dmitry Ustalov, Toloka, Belgrade, Serbia
- Saiph Savage, Northeastern University Boston, MA, USA
- Niels van Berkel, University of Aalborg Aalborg, Denmark
- Yang Liu, University of California Santa Cruz, Santa Cruz, USA
- Alisa Smirnova, Toloka, Lucerne, Switzerland

Don’t hesitate to contact us at research@toloka.ai if you have any questions or suggestions regarding this workshop.