Karamad: A Voice-based Crowd-sourcing Platform for Under-served Populations

要旨

Crowdsourcing enables the completion of large-scale and hard-to-automate 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.

著者
Shan M. Randhawa
New York University, Abu Dhabi, United Arab Emirates
Tallal Ahmad
Lahore University of Management Sciences, Lahore, Pakistan
Jay Chen
ICSI, Berkeley, California, United States
Agha Ali Raza
Lahore University of Management Sciences, Lahore, Punjab, Pakistan
DOI

10.1145/3411764.3445417

論文URL

https://doi.org/10.1145/3411764.3445417

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Various People

[A] Paper Room 13, 2021-05-11 17:00:00~2021-05-11 19:00:00 / [B] Paper Room 13, 2021-05-12 01:00:00~2021-05-12 03:00:00 / [C] Paper Room 13, 2021-05-12 09:00:00~2021-05-12 11:00:00
Paper Room 13
13 件の発表
2021-05-11 17:00:00
2021-05-11 19:00:00
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