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Crowd-work has increased significantly in recent years, particularly among women from Latin America. However, the specific needs and characteristics of this workforce have not been studied nearly enough. For this reason, we have conducted a series of surveys, questionnaires, and design sessions directly with Latin-American users of crowd-working platforms. Our aim was to create a system to empower crowd-workers with AI enhanced tools for their day-to-day tasks. As a result, we created a customized platform, La Independiente, and two web plugins. This project is unique in that it leverages gender perspective methodologies, AI powered-systems, and public policy analysis to design smart tools that are both professionally useful and culturally relevant.
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An effective online platform for crowd classification of coastal wetland loss
Abstract Wetland loss is increasing rapidly, and there are gaps in public awareness of the problem. By crowdsourcing image analysis of wetland morphology, academic and government studies could be supplemented and accelerated while engaging and educating the public. The Land Loss Lookout (LLL) project crowdsourced mapping of wetland morphology associated with wetland loss and restoration. We demonstrate that volunteers can be trained relatively easily online to identify characteristic wetland morphologies, or patterns present on the landscape that suggest a specific geomorphological process. Results from a case study in coastal Louisiana revealed strong agreement among nonexpert and expert assessments who agreed on classifications at least 83% and at most 94% of the time. Participants self‐reported increased knowledge of wetland loss after participating in the project. Crowd‐identified morphologies are consistent with expectations, although more work is needed to directly compare LLL results with previous studies. This work provides a foundation for using crowd‐based wetland loss analysis to increase public awareness of the issue, and to contribute to land surveys or train machine learning algorithms.
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- Award ID(s):
- 1816426
- PAR ID:
- 10390086
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Conservation Science and Practice
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2578-4854
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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