Karamad: A Voice-based Crowdsourcing Platform for Underserved Populations
Shan M. Randhawa, Tallal Ahmad, Jay Chen, Agha Ali Raza
摘要
Crowdsourcing enables the completion of large-scale and hard-toautomate tasks, while allowing people to earn money. However, 3.6 billion people -a workforce comprising 46.4% of the world population -who could benefit most from this source of income lack the access and literacy to use computers, smartphones, and the internet. In this paper we present Karamad, a voice-based crowdsourcing platform that allows workers in low-resource regions to complete crowd work using low-end phones and receive payments as mobile airtime balance. We explore the usefulness, scalability, and sustainability of Karamad in Pakistan through a 6-month deployment. Without any advertising, training, or airtime subsidies, Karamad organically engaged 725 workers who completed 3,939 tasks (involving 43,006 components) including translations, dataset generation, and surveys on demographics, accessibility, disability, health, employment, and literacy. Collectively, the workers produced a valuable service market for potential customers and included female, unemployed, non-literate, and blind users.
• Human-centered computing → Sound-based input / output.
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