Human-AI Interaction
What Lies Beneath? Exploring AI Model Updates
Unveiling the hidden impacts of AI model updates on user perception, behavior, and task performance in AI-infused systems.
Key Executive Impact Metrics
Our analysis reveals the critical impact of AI model updates on enterprise operations. Understanding these shifts is crucial for maintaining user trust and operational efficiency.
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User Detection Rate of Model Changes
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Avg. Seconds Less Spent per Session (New Model)
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Increased Likelihood of Positive Decisions (New Model)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
User Perception & Detection
Impact on Performance & Behavior
Folk Theories & Preferences
Participants struggled to distinguish underlying model changes: Across 1764 comparisons, only 48.87% accuracy was observed in detecting model changes, close to random guessing. This contradicts the hypothesis that users would distinguish changes in black-box systems.
Reliance on perceived accuracy over observable cues: Users primarily relied on their subjective assessment of model accuracy to distinguish models, not on observable characteristics like latency or result count (p > 0.05). Greater perceived accuracy differences correlated with higher detection likelihood (β = -0.89630, p < 0.001).
Minimal improvement in human-AI team performance: Despite the new model's higher accuracy, recall improved only marginally (62.64% vs. 56.05%), with precision and false positive rates remaining similar.
Underlying model influenced user behavior: The newer model led to 1 fewer comparison, 16.7 seconds less spent per session, and 11.6 fewer results scrolled. Participants were 46% more likely to make positive decisions with the new model. Perceived model changes had negligible impact on behavior, except for slightly increased exploratory scrolling when a switch was perceived (β = 2.04, p = 0.048).
New model generally preferred, but not unanimously: 7 out of 10 participants favored the new model for its accuracy and relevance. Some valued the old model's larger result set for exploratory discoveries. Many found large result sets overwhelming.
Trade-off between quantity and quality: Participants favoring efficiency gravitated to the new model's focused results, while those valuing more options preferred the old model.
Diverse and inconsistent folk theories: Users developed varied explanations for model behavior, often reflecting personal interpretations rather than objective understanding (e.g., how models handle facial features, occlusions, latency, age sensitivity, non-facial attributes).
Enterprise Process Flow
Silent AI Update Deployment
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User Encounters New Behavior
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Lack of Explicit Communication
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User Develops Folk Theories
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Impacts Trust & Performance
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Users' Accuracy in Detecting AI Model Changes
| Feature/Aspect | Old Model Perception | New Model Perception |
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| Occlusion Handling |
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| Search Latency |
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Case Study: Civil War Photo Sleuth (CWPS)
In a real-world deployment, CWPS users were given the option to toggle between the old and new facial recognition models. This allowed for direct comparison and revealed divergent user experiences.
While most participants preferred the new model for its higher precision and relevance, some valued the larger result set of the old model for enabling more exploratory searches.
Users developed nuanced but inconsistent folk theories about model behavior, highlighting the need for tailored communication strategies in AI-infused systems. This directly influences enterprise applications where user trust and performance are critical.
Advanced ROI Calculator
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Implementation Roadmap
A strategic roadmap for seamlessly integrating AI model updates and managing user adoption.
Phase 1: Initial Model Deployment & Baseline Assessment
Deploy the new AI model in a controlled environment to establish baseline performance metrics and identify immediate impacts on user workflows.
Phase 2: Targeted User Communication Strategy
Develop and implement clear communication for model updates, focusing on tangible benefits and potential changes in behavior. Avoid silent updates to prevent misaligned expectations.
Phase 3: Real-World Monitoring & Feedback Loop
Continuously monitor user interactions, gather feedback, and analyze folk theories. Use this data to refine communication and potentially provide in-context explanations.
Phase 4: Adaptive AI-Human Collaboration Design
Adjust system interfaces and workflows to align with updated model capabilities and user needs, fostering improved human-AI team performance.
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