| Autor: |
Agrawal, Chandan, Papanai, Ashish, White, Jerome |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Presented at Deployable AI Workshop at AAAI-2024 |
| Druh dokumentu: |
Working Paper |
| Popis: |
This paper describes and evaluates a multistage approach to AI deployment. Each stage involves a more accurate method of inference, yet engaging each comes with an increasing cost. In outlining the architecture, we present a method for quantifying model uncertainty that facilitates confident deferral decisions. The architecture is currently under active deployment to thousands of cotton farmers across India. The broader idea however is applicable to a growing sector of AI deployments in challenging low resources settings. |
| Databáze: |
arXiv |
| Externí odkaz: |
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