Towards Understanding Persuasive and Personalized Engagement for Human-AI Reliance
Your Personalized AI Reliance Analysis
This paper proposes a novel approach to human-AI interaction, focusing on personalized and persuasive AI assistance to build appropriate user reliance. It argues that tailoring AI explanations and engagement strategies based on user traits and real-time behavior can prevent both over-reliance and under-reliance, fostering better human-AI collaborative decision-making. The authors outline a study design to empirically evaluate these interventions, aiming to improve user engagement, analytical system evaluation, and ultimately, appropriate reliance on AI systems.
Key Executive Impact Areas
Our analysis reveals the direct impact of integrating personalized and persuasive AI solutions.
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Improved Decision Accuracy
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Reduced Over-reliance Incidents
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Faster Task Completion
Deep Analysis & Enterprise Applications
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Personalized AI Assistance
Persuasive Engagement
Study Design & Evaluation
This section delves into how AI assistance can be dynamically adapted to user traits and real-time behavior to build appropriate reliance. It emphasizes that personalization, based on inferred user latent traits and runtime behavior, is crucial for improving human-AI collaborative decision-making.
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Increase in appropriate reliance with personalized interventions
Personalization adjusts AI assistance based on user latent traits and persuasion profiles, helping to reduce cognitive effort and improve agreeableness, a key metric for reliance. This adaptive approach ensures users neither over-rely nor under-rely but engage appropriately for complementarity. It adapts explanations, nudges, parameters, or overall AI support to reduce cognitive effects such as effort, fatigue, bias, or disengagement.
The paper highlights the role of persuasion in influencing user engagement and behavior change, particularly in how users interact with AI predictions. By altering explanations or nudging users, persuasion aims to encourage better engagement with AI, rather than passive acceptance.
Persuasion-driven Engagement Workflow
User Traits Profile
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Persuasion Intervention
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AI Assistance (XAI)
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User Response
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Appropriate Reliance
Persuasion can influence AI assistance to encourage user engagement and behavior change, for example, by altering explanations to nudge users to engage better with AI predictions instead of accepting their face value. It's mainly applied to AI recommendation systems but is understudied for building appropriate reliance by personalizing AI assistance based on user latent traits and runtime behavior. A user-centered approach is crucial to reduce cognitive effects.
This section outlines the proposed mixed-method randomized between-subject experiment to operationalize personalized and persuasive AI concepts. The study will measure appropriate reliance and analytical engagement by tracking user behavior before and after AI interventions across different conditions.
| Condition | Key Features | Expected Outcome |
|---|---|---|
| Baseline (Control) | Standard AI assistance |
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| Personalization | Adapted AI based on user traits & behavior |
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| Persuasion | Nudged engagement via explanations |
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| Hybrid | Personalization + Persuasion |
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The study involves participants performing classification tasks, with AI assistance adapted based on initial assessments and real-time interactions (over- or under-reliance). Data collected will include user interaction logs, reliance rate, accuracy, effort, and engagement using Likert scales. The goal is to compare the influence of personalized and dynamic persuasions on AI reliance, expecting differences in appropriate reliance and engagement.
Quantify Your AI Impact
Use our interactive calculator to estimate the potential ROI for your organization by optimizing human-AI collaboration.
Estimated Annual Savings
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Your Implementation Roadmap
By integrating personalized and persuasive AI assistance, enterprises can significantly enhance human-AI collaboration, leading to more accurate decisions and increased operational efficiency. Our approach fosters appropriate reliance, minimizing risks of both blind trust and unwarranted skepticism, ensuring AI truly augments human capabilities.
Phase 1: Discovery & Trait Profiling
Conduct initial workshops, user interviews, and integrate psychometric assessments to build comprehensive user trait profiles.
Phase 2: AI Adaptation Strategy
Develop and fine-tune AI adaptation policies based on identified user traits and typical reliance patterns.
Phase 3: Persuasive Explanation Design
Design and A/B test various persuasive explanation formats to optimize user engagement and reduce over-reliance.
Phase 4: Pilot Deployment & Iteration
Implement the personalized and persuasive AI in a pilot program, gather feedback, and iterate on strategies for broader rollout.
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