Towards Human-AI Complementarity in Matching Tasks

TL;DR

This paper introduces comatch, a collaborative matching system that optimally combines human judgment and algorithmic confidence to improve decision quality in high-stakes matching tasks, validated through extensive human studies.

Contribution

It proposes a novel collaborative approach that defers decisions to humans or algorithms based on confidence, enhancing matching outcomes beyond existing systems.

Findings

Comatch outperforms human-only and algorithm-only matching in experiments.

The system effectively balances decision delegation to maximize performance.

Large-scale human study validates the approach's effectiveness.

Abstract

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.