# The Science of Rejection: A Research Area for Human Computation

arXiv: https://arxiv.org/abs/2111.06736 · HTML: https://mlsanity.com/paper/2111.06736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- [Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration](https://mlsanity.com/paper/1610.09064/) (2016)

## Built on by (most cited first, 1 of 1)

- [Rethinking and Recomputing the Value of Machine Learning Models](https://mlsanity.com/paper/2209.15157/) (2022)

## Abstract (arXiv)

We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.