# Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

arXiv: https://arxiv.org/abs/2310.02108 · HTML: https://mlsanity.com/paper/2310.02108/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 70 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [SCOUT: Self-aware Discriminant Counterfactual Explanations](https://mlsanity.com/paper/2004.07769/) (2020)
- [Does Explainable Artificial Intelligence Improve Human Decision-Making?](https://mlsanity.com/paper/2006.11194/) (2020)
- [Visual correspondence-based explanations improve AI robustness and human-AI team accuracy](https://mlsanity.com/paper/2208.00780/) (2022)
- [The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](https://mlsanity.com/paper/2008.09100/) (2020)
- [Near-Optimally Teaching the Crowd to Classify](https://mlsanity.com/paper/1402.2092/) (2014)
- [Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals](https://mlsanity.com/paper/2203.12892/) (2022)
- [A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making](https://mlsanity.com/paper/2205.05126/) (2022)
- [Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2204.06916/) (2022)
- [DEAL: Deep Evidential Active Learning for Image Classification](https://mlsanity.com/paper/2007.11344/) (2020)
- [Investigations of Performance and Bias in Human-AI Teamwork in Hiring](https://mlsanity.com/paper/2202.11812/) (2022)
- [Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior](https://mlsanity.com/paper/2002.08777/) (2020)
- [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022)
- [Improving Users' Mental Model with Attention-directed Counterfactual Edits](https://mlsanity.com/paper/2110.06863/) (2021)

## Built on by (most cited first, 8 of 8)

- [Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation](https://mlsanity.com/paper/2404.12056/) (2024)
- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [A Survey of AI Reliance](https://mlsanity.com/paper/2408.03948/) (2024)
- [Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human](https://mlsanity.com/paper/2606.08919/) (2026)
- [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024)
- [Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits](https://mlsanity.com/paper/2508.02328/) (2025)
- [Development of Mental Models in Human-AI Collaboration: A Conceptual Framework](https://mlsanity.com/paper/2510.08104/) (2025)
- [Beyond single-channel agentic benchmarking](https://mlsanity.com/paper/2602.18456/) (2026)

## Abstract (arXiv)

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.