mlsanityData to 7 Oct 2026

Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy

arXiv 2411.06211 ↗First posted 9 Nov 2024cs.AI cs.CYcited by 4

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Abstract (arXiv)

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 104 references that are in the map. First posted on arXiv 9 Nov 2024.

Builds on

17 of 104 references.

References · 17

  1. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  2. 2023Gender bias and stereotypes in Large Language Modelscited by 185
  3. 2023chatClimate: Grounding Conversational AI in Climate Sciencecited by 32
  4. 2022Trust in AI and Its Role in the Acceptance of AI Technologiescited by 29
  5. 2021A learning agent that acquires social norms from public sanctions in decentralized multi-agent settingscited by 24
  6. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  7. 2024Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classificationcited by 9
  8. 2022Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directionscited by 7
  9. 2023Fostering new Vertical and Horizontal IoT Applications with Intelligence Everywherecited by 3
  10. 2023Understanding the Vulnerability of CLIP to Image Compressioncited by 3
  11. 2021A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machinecited by 2
  12. 2023Agent Heterogeneity Mediates Extremism in an Adaptive Social Network Modelcited by 2
5 more
  1. 2020COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicinecited by 1
  2. 2021Collective decision-making under changing social environments among agents adapted to sparse connectivitycited by 1
  3. 2022Agent-Based Modelling for Urban Analytics: State of the Art and Challengescited by 1
  4. 2022Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matchingcited by 1
  5. 2023Understanding (Ir)rational Herding Onlinecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025BondBERT: What we learn when assigning sentiment in the bond marketcited by 1
  2. 2025We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergycited by 0
  3. 2026Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Marketcited by 0
  4. 2026Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Modelscited by 0