# AI-enhanced Collective Intelligence

arXiv: https://arxiv.org/abs/2403.10433 · HTML: https://mlsanity.com/paper/2403.10433/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 250 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 30 of 37)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [Are Emergent Abilities in Large Language Models just In-Context Learning?](https://mlsanity.com/paper/2309.01809/) (2023)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey](https://mlsanity.com/paper/1911.12116/) (2019)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition](https://mlsanity.com/paper/2105.08692/) (2021)
- [A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair](https://mlsanity.com/paper/2310.08879/) (2023)
- [Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2204.06916/) (2022)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [On the Effect of Information Asymmetry in Human-AI Teams](https://mlsanity.com/paper/2205.01467/) (2022)
- [Assigning AI: Seven Approaches for Students, with Prompts](https://mlsanity.com/paper/2306.10052/) (2023)
- [Who/What is My Teammate? Team Composition Considerations in Human-AI Teaming](https://mlsanity.com/paper/2105.11000/) (2021)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions](https://mlsanity.com/paper/2102.05460/) (2021)
- [Multilayer network simplification: approaches, models and methods](https://mlsanity.com/paper/2004.14808/) (2020)
- [Eigenvector centrality of nodes in multiplex networks](https://mlsanity.com/paper/1305.7445/) (2013)
- [Multilayer Networks in a Nutshell](https://mlsanity.com/paper/1804.03488/) (2018)
- [Even Good Bots Fight: The Case of Wikipedia](https://mlsanity.com/paper/1609.04285/) (2016)
- [Optimal incentives for collective intelligence](https://mlsanity.com/paper/1611.03899/) (2016)
- [When none of us perform better than all of us together: the role of analogical decision rules in groups](https://mlsanity.com/paper/1406.7562/) (2014)

## Built on by (most cited first, 10 of 10)

- [When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs](https://mlsanity.com/paper/2510.19107/) (2025)
- [When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration](https://mlsanity.com/paper/2506.05579/) (2025)
- [An AI Theory of Mind Will Enhance Our Collective Intelligence](https://mlsanity.com/paper/2411.09168/) (2024)
- [MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications](https://mlsanity.com/paper/2506.19502/) (2025)
- [An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem](https://mlsanity.com/paper/2507.22326/) (2025)
- [AI-in-the-loop: The future of biomedical visual analytics applications in the era of AI](https://mlsanity.com/paper/2412.15876/) (2024)
- [AI as intermediary in modern-day ritual: An immersive, interactive production of the roller disco musical Xanadu at UCLA](https://mlsanity.com/paper/2511.06195/) (2025)
- [Heterogeneous Multi-Agent Modeling for Measurement and Network Analysis of the Data Service Market](https://mlsanity.com/paper/2605.27433/) (2026)
- [AI Evaluation Should Work With Humans](https://mlsanity.com/paper/2608.13577/) (2026)
- [Discovering Adaptive Transmission Programs for Collective Innovation](https://mlsanity.com/paper/2608.24545/) (2026)

## Abstract (arXiv)

Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence. We conclude by considering potential challenges and future developments in this field.