# Coverage-Constrained Human-AI Cooperation with Multiple Experts

arXiv: https://arxiv.org/abs/2411.11976 · HTML: https://mlsanity.com/paper/2411.11976/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 96 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams](https://mlsanity.com/paper/2401.14432/) (2024)
- [Human and AI Perceptual Differences in Image Classification Errors](https://mlsanity.com/paper/2304.08733/) (2023)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Learning to Defer in Congested Systems: The AI-Human Interplay](https://mlsanity.com/paper/2402.12237/) (2024)
- [Learning to Complement with Multiple Humans](https://mlsanity.com/paper/2311.13172/) (2023)

## Built on by (most cited first, 5 of 5)

- [Budgeted Multiple-Expert Deferral](https://mlsanity.com/paper/2510.26706/) (2025)
- [Fatigue-Aware Learning to Defer via Constrained Optimisation](https://mlsanity.com/paper/2604.00904/) (2026)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models](https://mlsanity.com/paper/2505.19220/) (2025)
- [READY or Not: Reliable Enterprise Agent Deployment](https://mlsanity.com/paper/2609.02095/) (2026)

## Abstract (arXiv)

Human-AI cooperative classification (HAI-CC) approaches aim to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, a notable research gap remains in effectively exploring both L2D and L2C under diverse expert knowledge to improve decision-making, particularly when constrained by the cooperation cost required to achieve a target probability for AI-only selection (i.e., coverage). In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. CL2DC makes final decisions through either AI prediction alone or by deferring to or complementing a specific expert, depending on the input data. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Also, CL2DC is designed to address scenarios where training sets contain multiple noisy-label annotations without any clean-label references. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.