Reimagining Global Crowdsourcing for Better Human-AI Collaboration

Alorwu, Andy and Savage, Saiph and Van Berkel, Neils and Ustalov, Dmitry and Drutsa, Alexey and Oppenlaender, Jonas and Bates, Oliver and Hettiachchi, Danula and Gadiraju, Ujwal and Goncalves, Jorge and Hosio, Simo (2022) Reimagining Global Crowdsourcing for Better Human-AI Collaboration. In: CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. ACM, New York. ISBN 9781450391566

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Abstract

Crowdworkers silently enable much of today's AI-based products, with several online platforms offering a myriad of data labelling and content moderation tasks through convenient labour marketplaces. The HCI community has been increasingly interested in investigating the worker-centric issues inherent in the current model and seeking for potential improvements that could be implemented in the future. This workshop explores how a reimagined perspective on crowdsourcing platforms could provide a more equitable, fair, and rewarding experience. This includes not only the workers but also the platforms, who could benefit e.g. from better processes for worker onboarding, skills-development, and growth. We invite visionary takes in various formats on this topic to spread awareness of worker-centric research and developments to the CHI community. As a result of interactive ideation work in the workshop, we articulate a future direction roadmap for research centred around crowdsourcing platforms. Finally, as a specific interest area, the workshop seeks to study crowdwork from the context of the Global South, which has been arising as an important but critically understudied crowdsourcing market in recent years.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems http://doi.acm.org/10.1145/3491101.3503725
ID Code:
163505
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Deposited On:
22 Jun 2022 08:55
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Nov 2022 17:45