# Maintaining User Trust Through Multistage Uncertainty Aware Inference

arXiv: https://arxiv.org/abs/2402.00015 · HTML: https://mlsanity.com/paper/2402.00015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [A Case for Rejection in Low Resource ML Deployment](https://mlsanity.com/paper/2208.06359/) (2022)
- [BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India](https://mlsanity.com/paper/2304.00763/) (2023)

## Abstract (arXiv)

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.