# A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams

arXiv: https://arxiv.org/abs/2401.14432 · HTML: https://mlsanity.com/paper/2401.14432/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [A Unified Model for Multi-class Anomaly Detection](https://mlsanity.com/paper/2206.03687/) (2022)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2010.05903/) (2020)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Regression Under Human Assistance](https://mlsanity.com/paper/1909.02963/) (2019)

## Built on by (most cited first, 12 of 12)

- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025)
- [ReDit: Reward Dithering for Improved LLM Policy Optimization](https://mlsanity.com/paper/2506.18631/) (2025)
- [Coverage-Constrained Human-AI Cooperation with Multiple Experts](https://mlsanity.com/paper/2411.11976/) (2024)
- [Modeling Distinct Human Interaction in Web Agents](https://mlsanity.com/paper/2602.17588/) (2026)
- [LLMs Are Not Yet Ready for Deepfake Image Detection](https://mlsanity.com/paper/2506.10474/) (2025)
- [From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Users](https://mlsanity.com/paper/2508.07596/) (2025)
- [Human-AI Collaboration in Cloud Security: Cognitive Hierarchy-Driven Deep Reinforcement Learning](https://mlsanity.com/paper/2502.16054/) (2025)
- [AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support](https://mlsanity.com/paper/2602.15865/) (2026)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams](https://mlsanity.com/paper/2605.08388/) (2026)
- [A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes](https://mlsanity.com/paper/2608.16461/) (2026)

## Abstract (arXiv)

This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as rejection learning and learning to defer, A2C incorporates AI systems trained to recognise uncertainty in their decisions and defer to human experts when needed. Moreover, A2C caters to scenarios where even human experts encounter limitations, such as in incident detection and response in cyber Security Operations Centres (SOC). In such scenarios, A2C facilitates collaborative explorations, enabling collective resolution of complex challenges. With support for three distinct decision-making modes in human-AI teams: Automated, Augmented, and Collaborative, A2C offers a flexible platform for developing effective strategies for human-AI collaboration. By harnessing the strengths of both humans and AI, it significantly improves the efficiency and effectiveness of complex decision-making in dynamic and evolving environments. To validate A2C's capabilities, we conducted extensive simulative experiments using benchmark datasets. The results clearly demonstrate that all three modes of decision-making can be effectively supported by A2C. Most notably, collaborative exploration by (simulated) human experts and AI achieves superior performance compared to AI in isolation, underscoring the framework's potential to enhance decision-making within human-AI teams.