Enterprise AI Analysis
Balancing the Unknown: Exploring Human Reliance on AI Advice Under Aleatoric and Epistemic Uncertainty
Authors: Joshua Holstein, Lars Böcking, Philipp Spitzer, Niklas Kühl, Michael Vössing, Gerhard Satzger
Abstract: 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
This study provides critical insights for enterprise AI decision-making by revealing how the presentation and type of AI uncertainty influence human reliance and decision-making strategies.
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Research Participants
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Decisions Analyzed
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Uncertainty Types Explored
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Reliance Shift Observed
Key Research Questions
RQ1: How does human reliance on uncertain AI advice change when the uncertainty is specified as either epistemic or aleatoric?
RQ2: How does human reliance on uncertain AI advice change when the uncertainty is decomposed into its aleatoric and epistemic components?
RQ3: How does human reliance on uncertain AI advice differ between aleatoric and epistemic uncertainty when the overall uncertainty is decomposed into its components?
RQ4: How do prior beliefs influence human reliance on uncertain AI advice across different sources and degrees of uncertainty (aleatoric vs. epistemic)?
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Human perception and processing of uncertainty are fundamentally constrained by cognitive limitations and systematic biases. Individuals generally exhibit an aversion to uncertainty, preferring known probabilities over ambiguous ones, and often struggle with interpreting statistical and probabilistic information. This can manifest as either overconfidence or underconfidence depending on their perceived expertise.
In AI-assisted decision-making contexts, the opacity of AI systems compounds existing human limitations, influencing user reliance behavior. Understanding and measuring reliance requires domain-specific approaches, with prior beliefs creating systematic biases through confirmation bias and anchoring effects.
↓ Reliance
as Uncertainty Increases (H1 Supported)
Inherent randomness: Stems from inherent variability in outcomes or factors not captured in data. Cannot be reduced by gathering more examples for fixed variables.
Lack of knowledge: Arises from insufficient training data or overly simplistic models. Can be reduced by collecting more examples.
Prior Beliefs
Significantly shape reliance behavior (H4 Supported)
Enterprise Process Flow (Experiment 2: Decomposed Uncertainty)
Initial Estimate
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AI Advice (Decomposed Uncertainty)
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Revised Estimate
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Reliance Calculation
| Uncertainty Presentation | Observed Reliance Strategy | Key Takeaway |
|---|---|---|
| Single Source (Experiment 1) | Level-dependent differentiation (trend) | More sensitivity to epistemic uncertainty at higher levels; perceived as direct indicator of AI's capabilities. |
| Decomposed (Experiment 2) | Generic strategy | Uncertainty sources no longer differentiated; overall magnitude matters more. Leads to more critical advice rejection when uncertainty is high. |
Designing for Informed Human-AI Collaboration
This study highlights that the way uncertainty is presented matters significantly. Designers should carefully consider whether to present uncertainty sources in isolation (e.g., epistemic for medical diagnosis where knowledge gaps are critical) or decomposed (for tasks like real estate pricing where both inherent variability and model knowledge gaps are relevant).
The goal is to facilitate appropriate reliance and critical thinking, especially in high-uncertainty scenarios, and mitigate the impact of prior beliefs.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could realize by optimizing human-AI collaboration with tailored uncertainty communication.
Potential Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A typical journey to integrate uncertainty-aware AI for enhanced human collaboration within your enterprise.
01. Initial Assessment & Strategy
Conduct a comprehensive audit of existing AI systems, identify critical decision points, and define key performance indicators for improved human-AI collaboration and trust calibration.
02. Data & Model Preparation
Prepare datasets to enable robust uncertainty quantification (aleatoric and epistemic). Retrain or fine-tune AI models to explicitly output differentiated uncertainty estimates.
03. UI/UX Prototyping & Testing
Design and prototype interfaces for communicating uncertainty, leveraging insights from this research (e.g., contextualized textual cues, interactive visualizations). Conduct user studies with target end-users.
04. Pilot Deployment & Iteration
Deploy uncertainty-aware AI systems in a pilot environment. Collect feedback, measure reliance metrics, and iteratively refine communication strategies based on real-world usage and performance.
05. Full-Scale Rollout & Monitoring
Scale the solution across the enterprise. Establish continuous monitoring for AI performance, user reliance patterns, and decision quality, ensuring long-term appropriate human-AI collaboration.
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