mlsanityData to 7 Oct 2026

Effective Generative AI: The Human-Algorithm Centaur

arXiv 2406.10942 ↗First posted 16 Jun 2024cs.AI cs.LGcited by 8

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Abstract (arXiv)

Advanced analytics science methods have enabled combining the power of artificial and human intelligence, creating textitcentaurs that allow superior decision-making. Centaurs are hybrid human-algorithm models that combine both formal analytics and human intuition in a symbiotic manner within their learning and reasoning process. We argue that the future of AI development and use in many domains needs to focus more on centaurs as opposed to other AI approaches. This paradigm shift towards centaur-based AI methods raises some fundamental questions: How are centaurs different from other human-in-the-loop methods? What are the most effective methods for creating centaurs? When should centaurs be used, and when should the lead be given to pure AI models? Doesn't the incorporation of human intuition -- which at times can be misleading -- in centaurs' decision-making process degrade its performance compared to pure AI methods? This work aims to address these fundamental questions, focusing on recent advancements in generative AI, and especially in Large Language Models (LLMs), as a main case study to illustrate centaurs' critical essentiality to future AI endeavors.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 48 of its 142 references that are in the map. First posted on arXiv 16 Jun 2024.

Builds on

48 of 142 references.

References · 48

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 5.

8 papers.

  1. 2025Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approachcited by 22
  2. 2025Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristicscited by 3
  3. 2024Harmonizing Safety and Speed: A Human-Algorithm Approach to Enhance the FDA's Medical Device Clearance Policycited by 1
  4. 2026Autonomous Business System via Neuro-symbolic AIcited by 1
  5. 2026Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuningcited by 1
  6. 2026Leading Across the Spectrum of Human-AI Relationships: A Conceptual Framework for Increasingly Heterogeneous Teamscited by 0
  7. 2026Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligencecited by 0
  8. 2026Exploring Collaboration between a language and a non-language agentcited by 0

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