Maintaining User Trust Through Multistage Uncertainty Aware Inference
This addresses trust issues for users in low-resource settings like agriculture, though it appears incremental as it builds on existing uncertainty-aware methods.
The paper tackles the problem of maintaining user trust in AI deployments by proposing a multistage inference approach that balances accuracy and cost, with a method for quantifying model uncertainty to make deferral decisions, and it is currently deployed to thousands of cotton farmers in India.
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.