mlsanityData to 7 Oct 2026

AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems

arXiv 2402.06287 ↗First posted 9 Feb 2024cs.LG cs.AI cs.HCcited by 7

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Abstract (arXiv)

Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and the goals varied. This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 81 of its 208 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

81 of 208 references.

References · 81

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38 more
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50 most cited of 81 All 81 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.

  1. 2024A Causal Framework for Evaluating Deferring Systemscited by 13
  2. 2024Coverage-Constrained Human-AI Cooperation with Multiple Expertscited by 4
  3. 2026Strategic Decision Support for AI Agentscited by 1
  4. 2024Expert-aware uncertainty estimation for quality control of neural-based blood typingcited by 0
  5. 2024Bridging the Gap in Hybrid Decision-Making Systemscited by 0
  6. 2025FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelinescited by 0
  7. 2026Human-AI Teaming Through the Lens of Calibrationcited by 0