# AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions

arXiv: https://arxiv.org/abs/2304.08804 · HTML: https://mlsanity.com/paper/2304.08804/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 67 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [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)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- ["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)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Does Explainable Artificial Intelligence Improve Human Decision-Making?](https://mlsanity.com/paper/2006.11194/) (2020)
- [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 Collaboration With Descriptions of AI Behavior](https://mlsanity.com/paper/2301.06937/) (2023)
- [A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making](https://mlsanity.com/paper/2205.05126/) (2022)
- [Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2209.11812/) (2022)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making](https://mlsanity.com/paper/2208.04181/) (2022)
- [A Unified Bi-directional Model for Natural and Artificial Trust in Human-Robot Collaboration](https://mlsanity.com/paper/2106.02194/) (2021)

## Built on by (most cited first, 9 of 9)

- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making](https://mlsanity.com/paper/2401.07058/) (2024)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest](https://mlsanity.com/paper/2504.04243/) (2025)
- [Learned, Lagged, LLM-splained: LLM Responses to End User Security Questions](https://mlsanity.com/paper/2411.14571/) (2024)
- [A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice](https://mlsanity.com/paper/2606.06081/) (2026)
- [Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI](https://mlsanity.com/paper/2508.07872/) (2025)
- [Addressing the Synergy Gap: The Six Elements of the Design Space](https://mlsanity.com/paper/2605.21635/) (2026)
- [Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories](https://mlsanity.com/paper/2610.02673/) (2026)

## Abstract (arXiv)

In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In practice, however, we often see that humans cannot assess the correctness of AI recommendations and, as a result, adhere to wrong or override correct advice. Different ways of relying on AI recommendations have immediate, yet distinct, implications for decision quality. Unfortunately, reliance and decision quality are often inappropriately conflated in the current literature on AI-assisted decision-making. In this work, we disentangle and formalize the relationship between reliance and decision quality, and we characterize the conditions under which human-AI complementarity is achievable. To illustrate how reliance and decision quality relate to one another, we propose a visual framework and demonstrate its usefulness for interpreting empirical findings, including the effects of interventions like explanations. Overall, our research highlights the importance of distinguishing between reliance behavior and decision quality in AI-assisted decision-making.