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

arXiv: https://arxiv.org/abs/2401.04729 · HTML: https://mlsanity.com/paper/2401.04729/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 86 references that are in the map.
- First posted on arXiv 9 Jan 2024.

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

- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [Differentiable Learning Under Triage](https://mlsanity.com/paper/2103.08902/) (2021)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022)
- [Human-in-the-loop Artificial Intelligence](https://mlsanity.com/paper/1710.08191/) (2017)
- [Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts](https://mlsanity.com/paper/2206.07948/) (2022)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019)
- [Improving Human-AI Collaboration With Descriptions of AI Behavior](https://mlsanity.com/paper/2301.06937/) (2023)
- [Role of Human-AI Interaction in Selective Prediction](https://mlsanity.com/paper/2112.06751/) (2021)
- [Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction](https://mlsanity.com/paper/2303.09224/) (2023)
- [The Utility of Explainable AI in Ad Hoc Human-Machine Teaming](https://mlsanity.com/paper/2209.03943/) (2022)
- [A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making](https://mlsanity.com/paper/2205.05126/) (2022)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables](https://mlsanity.com/paper/2207.13834/) (2022)
- [On the Influence of Explainable AI on Automation Bias](https://mlsanity.com/paper/2204.08859/) (2022)
- [On the Effect of Information Asymmetry in Human-AI Teams](https://mlsanity.com/paper/2205.01467/) (2022)
- [Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools](https://mlsanity.com/paper/2303.00192/) (2023)
- [Human-AI Collaboration in Decision-Making: Beyond Learning to Defer](https://mlsanity.com/paper/2206.13202/) (2022)
- [Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study](https://mlsanity.com/paper/2304.08861/) (2023)
- [The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions](https://mlsanity.com/paper/2102.05460/) (2021)
- [Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge](https://mlsanity.com/paper/2308.15700/) (2023)

## Built on by (most cited first, 5 of 5)

- [Training Proactive and Personalized LLM Agents](https://mlsanity.com/paper/2511.02208/) (2025)
- [Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability](https://mlsanity.com/paper/2504.16056/) (2025)
- [AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agents](https://mlsanity.com/paper/2604.20279/) (2026)
- [Development of Mental Models in Human-AI Collaboration: A Conceptual Framework](https://mlsanity.com/paper/2510.08104/) (2025)
- [Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning](https://mlsanity.com/paper/2512.11276/) (2025)

## 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.