# Improving Learning-to-Defer Algorithms Through Fine-Tuning

arXiv: https://arxiv.org/abs/2112.10768 · HTML: https://mlsanity.com/paper/2112.10768/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Human uncertainty makes classification more robust](https://mlsanity.com/paper/1908.07086/) (2019)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [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)
- [Differentiable Learning Under Triage](https://mlsanity.com/paper/2103.08902/) (2021)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Classification Under Human Assistance](https://mlsanity.com/paper/2006.11845/) (2020)

## Built on by (most cited first, 11 of 11)

- [Who Should Predict? Exact Algorithms For Learning to Defer to Humans](https://mlsanity.com/paper/2301.06197/) (2023)
- [Sample Efficient Learning of Predictors that Complement Humans](https://mlsanity.com/paper/2207.09584/) (2022)
- [Principled Approaches for Learning to Defer with Multiple Experts](https://mlsanity.com/paper/2310.14774/) (2023)
- [Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer](https://mlsanity.com/paper/2407.13732/) (2024)
- [Learning to Defer with Limited Expert Predictions](https://mlsanity.com/paper/2304.07306/) (2023)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Consistent Learning-to-Defer with Expert-Conditional Advice](https://mlsanity.com/paper/2603.14324/) (2026)
- [Optimized Deferral for Imbalanced Settings](https://mlsanity.com/paper/2604.27723/) (2026)
- [Bayesian Inference for Correlated Human Experts and Classifiers](https://mlsanity.com/paper/2506.05636/) (2025)
- [Learning To Defer To A Population With Limited Demonstrations](https://mlsanity.com/paper/2510.19351/) (2025)
- [A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral](https://mlsanity.com/paper/2610.07084/) (2026)

## Abstract (arXiv)

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.