# Towards Human-AI Complementarity in Matching Tasks

arXiv: https://arxiv.org/abs/2508.13285 · HTML: https://mlsanity.com/paper/2508.13285/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (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)
- [Who Should Predict? Exact Algorithms For Learning to Defer to Humans](https://mlsanity.com/paper/2301.06197/) (2023)
- [Improving Expert Predictions with Conformal Prediction](https://mlsanity.com/paper/2201.12006/) (2022)
- [MNL-Bandit: A Dynamic Learning Approach to Assortment Selection](https://mlsanity.com/paper/1706.03880/) (2017)
- [Sample Efficient Learning of Predictors that Complement Humans](https://mlsanity.com/paper/2207.09584/) (2022)
- [Classification Under Human Assistance](https://mlsanity.com/paper/2006.11845/) (2020)
- [Regression Under Human Assistance](https://mlsanity.com/paper/1909.02963/) (2019)
- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [Human-Aligned Calibration for AI-Assisted Decision Making](https://mlsanity.com/paper/2306.00074/) (2023)
- [Designing Decision Support Systems Using Counterfactual Prediction Sets](https://mlsanity.com/paper/2306.03928/) (2023)
- [Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making](https://mlsanity.com/paper/2005.10960/) (2020)
- [Towards Human-AI Complementarity with Prediction Sets](https://mlsanity.com/paper/2405.17544/) (2024)
- [Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/1801.10242/) (2018)
- [Reinforcement Learning Under Algorithmic Triage](https://mlsanity.com/paper/2109.11328/) (2021)
- [Auditing for Human Expertise](https://mlsanity.com/paper/2306.01646/) (2023)
- [Optimal Kidney Exchange with Immunosuppressants](https://mlsanity.com/paper/2103.02253/) (2021)
- [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023)
- [Dynamic Placement in Refugee Resettlement](https://mlsanity.com/paper/2105.14388/) (2021)
- [Matching Algorithms for Blood Donation](https://mlsanity.com/paper/2108.04862/) (2021)
- [Group fairness in dynamic refugee assignment](https://mlsanity.com/paper/2301.10642/) (2023)
- [Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes](https://mlsanity.com/paper/2407.13052/) (2024)

## Built on by (most cited first, 7 of 7)

- [Human-AI Collaborative Uncertainty Quantification](https://mlsanity.com/paper/2510.23476/) (2025)
- [Narrowing Action Choices with AI Improves Human Sequential Decisions](https://mlsanity.com/paper/2510.16097/) (2025)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making](https://mlsanity.com/paper/2602.19458/) (2026)
- [Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models](https://mlsanity.com/paper/2605.18202/) (2026)
- [CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks](https://mlsanity.com/paper/2606.09833/) (2026)
- [Strategic Decision Support for AI Agents](https://mlsanity.com/paper/2606.12587/) (2026)

## Abstract (arXiv)

Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthcare and social service provision. However, existing systems are not designed to achieve human-AI complementarity: decisions made by a human using an algorithmic matching system are not necessarily better than those made by the human or by the algorithm alone. Our work aims to address this gap. To this end, we propose collaborative matching (comatch), a data-driven algorithmic matching system that takes a collaborative approach: rather than making all the matching decisions for a matching task like existing systems, it selects only the decisions that it is the most confident in, deferring the rest to the human decision maker. In the process, comatch optimizes how many decisions it makes and how many it defers to the human decision maker to provably maximize performance. We conduct a large-scale human subject study with 800 participants to validate the proposed approach. The results demonstrate that the matching outcomes produced by comatch outperform those generated by either human participants or by algorithmic matching on their own. The data gathered in our human subject study and an implementation of our system are available as open source at https://github.com/Networks-Learning/human-AI-complementarity-matching.