Despite the obvious advantages, automation driven by machine learning and artificial intelligence carries pitfalls for the lives of millions of people. The pitfalls include disappearance of many well-established mass professions and increasing consumption of labeled data produced by humans. Those data suppliers are often managed by old fashioned approach and have to work full-time on routine pre-assigned task types. Crowdsourcing methodology can be considered as a modern and effective way to overcome these issues since it provides flexibility and freedom for task executors in terms of place, time and the task type they want to work on. However, many potential stakeholders of crowdsourcing processes hesitate to use this technology due to a series of doubts (that have not been removed during the past decade). In order to overcome this, we organize this workshop which will focus research and industry communities on three important aspects: Remoteness, Fairness, and Mechanisms.

We invite high-quality submissions one or more of the following areas in crowd science (both theoretical and practical contributions are welcome). Our (non-exhaustive) list of topics of interest include:

- Effectiveness and efficiency of remote work
- Fairness
- Mechanisms
- Automation with data supply powered by humans

A more complete list of topics is available at https://research.yandex.com/workshops/crowd/neurips-2020/cfp.

There are two types of submissions:

"regular" papers that provide a validated scientific contribution to crowd science;
work-in-progress or vision papers that propose ideas or present preliminary results.
In both cases, a paper can provide research contributions (theoretical and applied) or can describe designs and implementations of solutions/systems for practical tasks in some industry. In the latter case, papers solve or advance the understanding of ​issues related to deploying technologies based on human data supply for automation in the real world.

Papers should be submitted via EasyChair by the end of the deadline day Oct 02, 2020 (AOE). 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.

- https://easychair.org/conferences/?conf=neurips2020crowd

In all cases, a submitted paper must describe new, unpublished research, and must not have been published or under review elsewhere.

Each paper will be reviewed by at least three members of the program committee. Reviewers will be instructed to evaluate paper submissions according to specific review criteria. We encourage authors to review them before submission. The reviewing process will be double blind at the level of reviewers (i.e., reviewers cannot see author identities) but not at the level of workshop organizers. 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 (even in the supplementary material).

At least one author of each accepted paper must register for the main conference (NeurIPS 2020) to present the work or acceptance will be withdrawn.

Note: NeurIPS 2020 is a virtual-only conference.