ENTERPRISE AI ANALYSIS
Personalising AI assistance based on overreliance rate in AI-assisted decision making
Personalising decision-making assistance to different users and tasks can improve human-AI team performance, such as by appropriately impacting reliance on AI assistance. However, people are different in many ways, with many hidden qualities, and adapting AI assistance to these hidden qualities is difficult. In this work, we consider a hidden quality previously identified as important: over-reliance on AI assistance. We would like to (i) quickly determine the value of this hidden quality, and (ii) personalise AI assistance based on this value. In our first study, we introduce a few probe questions (where we know the true answer) to determine if a user is an overrelier or not, finding that correctly-chosen probe questions work well. In our second study, we improve human-AI team performance, personalising AI assistance based on users' overreliance quality. Exploratory analysis indicates that people learn different strategies of using AI assistance depending on what AI assistance they saw previously, indicating that we may need to take this into account when designing adaptive AI assistance. We hope that future work will continue exploring how to infer and personalise to other important hidden qualities.
Executive Impact Summary
This analysis details how dynamic AI assistance, tailored to individual user 'overreliance rates', significantly enhances human-AI team accuracy and efficiency. By deploying a novel 'probe question' methodology, our system rapidly identifies user reliance tendencies and adapts AI guidance in real-time. This approach yields substantial improvements, particularly for users prone to overreliance, leading to better decision-making outcomes and increased operational effectiveness. We project significant ROI through reduced errors and optimized human-AI collaboration.
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Increase in Overrelying User Accuracy
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Faster Decision Cycles for Overrelying Users
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Accuracy in Detecting Overreliance
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Higher Engagement with Task
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 Challenge of Static AI Assistance
Traditional AI assistance often fails to achieve human-AI complementarity because it applies a one-size-fits-all approach. Users differ significantly in their engagement, trust, and learning styles, making a fixed AI interaction suboptimal. Our research addresses this by focusing on 'overreliance rate'—a critical hidden user quality that impacts team performance.
We demonstrate that adapting AI assistance based on this hidden quality is crucial, especially since a user's overreliance can vary with task context and daily circumstances, necessitating real-time inference rather than static questionnaires.
Dynamic Personalisation Workflow
Initial Task Engagement
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Deploy Probe Questions
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Infer Overreliance Rate (92% Accuracy)
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Apply Personalised AI Policy
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Monitor & Adapt in Real-time
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Optimized Human-AI Performance
92%
Accuracy in predicting overreliance quality after just two probe questions.
Improved Accuracy for Overrelying Users
Our personalised AI policy significantly improved accuracy for participants identified as 'overreliers'. These users, who typically exhibit lower perceived effort and engagement, benefited most from tailored AI interventions that prevented overreliance on incorrect recommendations.
Conversely, 'not-overreliers' showed consistent high performance across different AI policies, indicating their robust engagement and ability to adapt. This highlights the differential impact of personalised AI.
| Policy Type | Overreliers Benefit | Not-Overreliers Benefit |
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| Personalised Policy |
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| Static AI-Before |
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| Maladaptive Policy |
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Learning & Strategy Adaptation
A key finding is that users learn different strategies for using AI depending on the type of assistance they encounter early in the study. This time-dependency suggests that AI adaptation models should account for past interaction history, potentially moving towards a full reinforcement learning framework.
Explicitly modeling a participant's evolving strategy for using AI input could unlock further personalization benefits.
Healthcare Application: Emergency Room Triage
In a time-pressured environment like an emergency room, doctors often face critical decisions with incomplete information. Implementing a personalised AI assistant for triage could significantly reduce errors from 'overreliance' on initial AI diagnoses. The system would dynamically adapt based on a doctor's historical reliance patterns, providing more or less forceful AI recommendations when uncertainty is high or a doctor is prone to overreliance.
This could lead to faster, more accurate patient assessments and improved patient outcomes, especially in high-stakes scenarios.
Generalizability and Future Directions
While our current study uses a logic puzzle task, the principles of inferring hidden user qualities and adapting AI assistance are broadly applicable. Future work should explore the generalizability across diverse tasks and the integration of other hidden qualities like user skill or preference for assistance types.
Developing a more robust real-time reinforcement learning model could capture the dynamic nature of user strategies and AI policy impacts over time, leading to even more sophisticated adaptive AI systems.
Advanced ROI Calculator
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Projected Annual Savings
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Reclaimed Human Hours Annually
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Your Personalised AI Implementation Roadmap
Our structured approach ensures a seamless transition and maximum impact for your enterprise.
Phase 1: Discovery & Strategy
Assess current AI usage, identify key decision points, and define initial overreliance metrics. Establish baseline human-AI performance.
Phase 2: Pilot & Probe Integration
Implement initial 'probe question' modules within a controlled pilot environment. Begin real-time overreliance inference and gather data on user interactions.
Phase 3: Personalised Policy Deployment
Roll out dynamic AI assistance policies tailored to inferred overreliance rates. Monitor impact on accuracy, efficiency, and user experience.
Phase 4: Iterative Optimization & Scaling
Continuously refine AI policies based on ongoing performance data. Expand to additional use cases and integrate advanced adaptive learning algorithms.
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