Towards Human-AI Complementarity in Matching Tasks
Authors: Arnaiz-Rodríguez, A. , Corvelo Benz, N., Thejaswi, S., Oliver, N. , Gomez Rodriguez, M.
External link: https://arxiv.org/abs/2508.13285
Publication: The Third Workshop on Hybrid Human-Machine Learning and Decision Making (HLDM'25) at ECML-PKDD 2025, 2025
DOI: https://arxiv.org/abs/2508.13285
PDF: Click here for the PDF paper
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 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.
Accepted
- ORAL at the HLDM’25 workshop (The Third Workshop on Hybrid Human-Machine Learning and Decision Making) at ECML-PKDD 2025 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases). Oporto, Portugal, September 2025.***
- Poster presentation and a Spotlight Lightning Talk in AI-SDM Workshop on Human-AI Complementarity for Decision Making at CMU.
This work was supported by the European Commission under Horizon Europe Programme, grant number 101120237 - ELIAS, by Intel corporation, a nominal grant received at the ELLIS Unit Alicante Foundation from the Regional Government of Valencia in Spain (Convenio Singular signed with Generalitat Valenciana, Conselleria de Innovación, Industria, Comercio y Turismo, Dirección General de Innovación) and a grant by the Banc Sabadell Foundation.