Enterprise AI Analysis
Clarifying or Complicating?: Understanding Older Adults' Engagement with Real-World XAI in E-Commerce
This study investigates how older adults perceive and interact with explainable AI (XAI) features in real-world e-commerce. Findings reveal that XAI's effectiveness is not uniform, with many older adults overlooking or misunderstanding explanation features. Global explanations elicited polarized responses, while local explanations grounded in user behavior recalibrated skepticism. A user-model dashboard exposed tensions between empowerment and surveillance. We propose actionable design strategies for inclusive and adaptive XAI systems.
Executive Impact
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Older Adults Studied
XAI Features Examined
Trust Calibration Improved (Est.)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Awareness
Evaluation
Transparency
Awareness: Invisible vs. Seen-but-Ignored
Many older adults did not notice personalized recommendations or explanation features, often mistaking them for generic advertising. This highlights a need for clearer visual differentiation and explicit signaling of personalization.
Evaluation: System-Level Awareness vs. Item-Level Judgment
Global explanations provided system-level understanding but did not help with concrete item-level judgments, leading to polarized trust. Local explanations, however, allowed for better relevance assessment.
Transparency: Seeking Control vs. Feeling Exposed
The user-model dashboard improved understanding and a sense of control for some, while others felt exposed by the extent of data collection. Transparency needs to be an adjustable interface for intervention, not an all-or-nothing reveal.
54%
of participants did not notice personalized recommendations or mistook them for advertising.
Enterprise Process Flow: Older Adults' XAI Engagement
Initial Exposure
→
Noticed/Ignored
→
Guided Interaction
→
Evaluated Explanations
→
Perceived Control/Surveillance
| Tension | Our Approach (Design Strategy) | Traditional Approach (Common Issues) |
|---|---|---|
| Awareness |
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| Evaluation |
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| Transparency |
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Understanding XAI Engagement: A Deeper Look
This study revealed that older adults' engagement with Explainable AI (XAI) features in e-commerce is complex and non-uniform. While some found explanations helpful for understanding and control, others overlooked them or interpreted them as marketing.
Key Highlights:
- XAI not uniformly beneficial: The same features had different impacts on different users.
- Polarized trust responses: Global explanations could either foster authority-based trust or increase skepticism.
- Need for adaptive design strategies: XAI must be aligned with users' capacities, goals, and comfort boundaries.
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