Enterprise AI Analysis
Balancing the Unknown: Exploring Human Reliance on AI Advice Under Aleatoric and Epistemic Uncertainty
Joshua Holstein, Lars Böcking, Philipp Spitzer, Niklas Kühl, Michael Vössing, Gerhard Satzger
Artificial intelligence systems increasingly support decision-making across a broad range of domains. The complexity of real-world tasks, however, introduces uncertainty into the prediction capabilities of these systems. This uncertainty can manifest as aleatoric uncertainty arising from inherent variability in outcomes or epistemic uncertainty stemming from limitations in the AI system's knowledge. While prior research has investigated uncertainty as a monolithic concept, the distinct effects of communicating aleatoric or epistemic uncertainty on humans and their reliance behavior remain unexplored. In this work, we present two behavioral experiments that systematically examine how participants rely on AI advice when faced with different types of uncertainty. While the first experiment manipulates the source of uncertainty, specifying it as either aleatoric or epistemic, the second decomposes uncertainty into its individual components, presenting aleatoric and epistemic uncertainty simultaneously. This work contributes to a deeper understanding of the multifaceted impact of different uncertainty types on human-AI interaction.
Executive Impact: Key Findings at a Glance
This study reveals that while overall reliance on AI advice decreases with increasing uncertainty, the framing and decomposition of uncertainty significantly influence human behavior. When presented with a single source, humans show a level-dependent differentiation strategy, but this distinction diminishes when both aleatoric and epistemic uncertainties are presented simultaneously. Prior beliefs consistently shape reliance, with uncertainty degree moderating this effect, but not the source of uncertainty itself. These findings underscore the need for sophisticated AI uncertainty communication.
0.876
Average AI Advice Reliance Coefficient (Initial State)
-0.338
Uncertainty Effect on Reliance (Coefficient)
-0.574
Prior Beliefs Confirmed Effect on Reliance (Coefficient)
20%
Increased Criticality with Decomposed Uncertainty (Qualitative)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The paper introduces the growing importance of AI in decision-making and the challenge of uncertainty in AI predictions. It highlights the distinction between aleatoric and epistemic uncertainty, which past research has often treated as a monolithic concept. The study aims to understand how humans rely on AI advice under different types and presentations of uncertainty.
This section establishes the core concepts. Aleatoric uncertainty arises from inherent variability and cannot be reduced by more data for a fixed set of variables. Epistemic uncertainty stems from limited knowledge or data and can potentially be reduced by more data. The paper emphasizes that understanding these distinct sources is crucial for effective AI-assisted decision-making.
The study utilized two behavioral experiments using a real estate price estimation task to investigate human reliance on AI advice under different uncertainty conditions. AI advice was simulated using prediction intervals to communicate uncertainty, framed either as a single source or decomposed into its components.
Experiment 1 Procedure Flow: Framing Uncertainty
Uncertainty Tutorial
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Comprehension Check
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Task Introduction
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Housing Price Estimation (12 Instances)
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Post-Task Questionnaire
Experiment 2 Procedure Flow: Decomposing Uncertainty
Uncertainty Tutorial
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Comprehension Check
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Task Introduction
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Housing Price Estimation (16 Instances)
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Post-Task Questionnaire
Experiment 1 focused on how framing uncertainty as either aleatoric or epistemic influences reliance.
-0.338
Uncertainty Effect on Reliance (Coefficient)
Across all conditions in Experiment 1, a significant negative effect for uncertainty (β = -0.338, p < 0.001) was observed, indicating that reliance on AI advice decreases as uncertainty grows. This aligns with the notion that decision-makers value more precise information.
0.954
P-value for Treatment Effect (Higher is less significant)
In Experiment 1, no statistically significant effect was found for the treatment condition (β = -0.003, p = 0.954). This suggests participants did not weigh aleatoric and epistemic uncertainty differently when presented as a single source.
-0.574
Prior Beliefs Confirmed Effect on Reliance (Coefficient)
Confirmation of prior beliefs significantly reduced WOA (β = -0.574, p < 0.001). When initial estimates align with AI advice, participants perceive less need to adjust, leading to lower reliance.
Experiment 2 explored the effects of decomposing overall uncertainty into its aleatoric and epistemic components.
0.315
P-value for Overall Reliance Difference (Higher is less significant)
In Experiment 2, comparing the combined vs. decomposed uncertainty treatments, the Mann-Whitney U test showed no significant difference in average WOA (μ = 0.4752 vs 0.4464, p = 0.315).
Decomposed Uncertainty Leads to More Critical Evaluation
An exploratory analysis in Experiment 2 revealed that participants more frequently reject advice when high uncertainty is presented in its decomposed form (aleatoric p_adj = 0.026, epistemic p_adj = 0.053). This suggests that making uncertainty sources explicit enables participants to critically evaluate AI advice and be more selective.
This finding highlights that explicit communication of uncertainty sources (aleatoric and epistemic) empowers users to engage more critically with AI advice, especially in high-risk scenarios. Instead of blindly accepting, users become more discerning, leading to more frequent rejections when the AI's confidence is low in its underlying knowledge or data variability. This is a crucial step towards fostering appropriate reliance.
| Uncertainty Level Comparison | Aleatoric p-value | Epistemic p-value |
|---|---|---|
| Very Low - Low | p < 0.001 | p = 0.033 |
| Low - High | p = 0.009 | p = 0.027 |
| High - Very High | p < 0.001 | p = 0.003 |
| Note: These values from Experiment 2 (Table 3 in the paper) indicate significant decreases in reliance between adjacent levels for both types of uncertainty. However, the comparative analysis of slopes (Table 5 in the paper) found no statistically significant differences between how humans react to increasing aleatoric vs. epistemic uncertainty when decomposed, implying they treat them similarly in terms of the rate of decline. | ||
0.063
P-value for Prior Beliefs x Very High Epistemic Interaction (Trend)
Experiment 2 revealed non-significant trends between participants' prior beliefs and high levels of epistemic uncertainty (High: β = 0.126, p = 0.095; Very High: β = 0.152, p = 0.063). This suggests that when prior beliefs are confirmed, high epistemic uncertainty *may* moderate and weaken the tendency for participants to favor initial judgments, but no significant interactions were observed for aleatoric uncertainty.
The discussion synthesizes findings across both experiments, highlighting how the context and presentation of uncertainty influence human reliance strategies, and provides practical design implications for AI systems.
Contextual Impact on Reliance Strategies
The study highlights that humans adopt different reliance strategies based on how uncertainty is presented. A 'level-dependent differentiation strategy' is observed when a single source of uncertainty is shown, but this shifts to a 'generic strategy' when both sources are jointly presented.
Our research demonstrates that the way uncertainty information is presented significantly influences human perception and subsequent reliance on AI advice. When epistemic uncertainty is isolated (Experiment 1), it's seen as a direct indicator of AI system's knowledge and capabilities, leading to a steeper decline in reliance. However, when both aleatoric and epistemic sources are presented simultaneously (Experiment 2), the distinction diminishes, and users respond to overall uncertainty magnitude. This emphasizes the critical need for designers to carefully consider not just *what* uncertainty information to present, but *how* to frame it within the broader decision-making context to avoid unintended shifts in user reliance strategies.
Designing for Effective Uncertainty Communication
The findings underscore the need for careful selection and presentation of uncertainty. Presenting epistemic uncertainty alone might be preferable for high-risk, unknown cases (e.g., medical diagnosis), while both aleatoric and epistemic uncertainty are relevant for tasks with inherent variability (e.g., real estate pricing).
AI-assisted decision support systems must carefully communicate uncertainty. For critical domains like medical diagnosis, presenting *only* epistemic uncertainty can effectively alert users to model limitations and prompt expert consultation, fostering appropriate caution. In tasks with inherent variability like real estate, both aleatoric (market volatility) and epistemic (model knowledge gaps) uncertainty can provide a holistic view. Future systems could employ layered methods, presenting high-level uncertainty first, then allowing users to drill down into specific sources, ensuring tailored reliance strategies and supporting more informed decisions.
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