Abstract: Despite the obvious advantages, automation driven by machine learning and artificial intelligence carries pitfalls for the lives of millions of people: disappearance of many well-established mass professions and consumption of labeled data that are produced by humans managed by out of time approach with full-time office work and pre-planned task types. Crowdsourcing methodology can be considered as an effective way to overcome these issues since it provides freedom for task executors in terms of place, time and which task type they want to work on. However, many potential participants of crowdsourcing processes hesitate to use this technology due to a series of doubts (that have not been removed during the past decade).

This workshop brings together people studying research questions on

(a) quality and effectiveness in remote crowd work;
(b) fairness and quality of life at work, tackling issues such as fair task assignment, fair work conditions, and on providing opportunities for growth; and
(c) economic mechanisms that incentivize quality and effectiveness for requester while maintaining a high level of quality and fairness for crowd performers (also known as workers).

Because quality, fairness and opportunities for crowd workers are central to our workshop, we will invite a diverse group of crowd workers from a global public crowdsourcing platform to our panel-led discussion.

Workshop web site: https://research.yandex.com/workshops/crowd/neurips-2020

Paper submission portal: https://easychair.org/conferences/?conf=neurips2020crowd

All submissions must be in PDF format. The page limit is up to eight (8) pages maximum for regular papers and four (4) pages for work-in-progress/vision papers. These limits are for main content pages, including all figures and tables. Additional pages containing appendices, acknowledgements, funding disclosures, and references are allowed. You must format your submission using the NeurIPS 2020 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review.

As an author, you are responsible for anonymizing your submission. In particular, you should not include author names, author affiliations, or acknowledgements in your submission and you should avoid providing any other identifying information.