mlsanityData to 7 Oct 2026

Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information

arXiv 2401.04729 ↗First posted 9 Jan 2024cs.HC cs.LGcited by 5

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Abstract (arXiv)

The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI. However, enabling humans to delegate instances effectively requires them to assess several factors. One key factor is the analysis of both their own capabilities and those of the AI in the context of the given task. In this work, we conduct a behavioral study to explore the effects of providing contextual information to support this delegation decision. Specifically, we investigate how contextual information about the AI and the task domain influence humans' delegation decisions to an AI and their impact on the human-AI team performance. Our findings reveal that access to contextual information significantly improves human-AI team performance in delegation settings. Finally, we show that the delegation behavior changes with the different types of contextual information. Overall, this research advances the understanding of computer-supported, collaborative work and provides actionable insights for designing more effective collaborative systems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 30 of its 86 references that are in the map. First posted on arXiv 9 Jan 2024.

Builds on

30 of 86 references.

References · 30

  1. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  3. 2020Consistent Estimators for Learning to Defer to an Expertcited by 262
  4. 2020Learning to Complement Humanscited by 123
  5. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  6. 2019The Algorithmic Automation Problem: Prediction, Triage, and Human Effortcited by 101
  7. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
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  9. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  10. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 71
  11. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
  12. 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
18 more
  1. 2017Human-in-the-loop Artificial Intelligencecited by 40
  2. 2022Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Expertscited by 40
  3. 2022Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Supportcited by 37
  4. 2019Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegabilitycited by 36
  5. 2023Improving Human-AI Collaboration With Descriptions of AI Behaviorcited by 35
  6. 2021Role of Human-AI Interaction in Selective Predictioncited by 33
  7. 2023Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfactioncited by 32
  8. 2022The Utility of Explainable AI in Ad Hoc Human-Machine Teamingcited by 30
  9. 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
  10. 2023The Impact of Imperfect XAI on Human-AI Decision-Makingcited by 21
  11. 2022Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservablescited by 19
  12. 2022On the Influence of Explainable AI on Automation Biascited by 18
  13. 2022On the Effect of Information Asymmetry in Human-AI Teamscited by 17
  14. 2022Human-AI Collaboration in Decision-Making: Beyond Learning to Defercited by 16
  15. 2023Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Toolscited by 16
  16. 2023Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Studycited by 15
  17. 2021The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionscited by 9
  18. 2023Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledgecited by 6

Built on by

Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025Training Proactive and Personalized LLM Agentscited by 42
  2. 2025Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainabilitycited by 2
  3. 2026AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agentscited by 1
  4. 2025Development of Mental Models in Human-AI Collaboration: A Conceptual Frameworkcited by 0
  5. 2025Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planningcited by 0