Rethinking User Empowerment in AI Recommender Systems: Designing through Transparency and Control

2025-09-14

AI Summary

Contemporary AI recommendation systems undermine user agency due to algorithmic opacity and unidirectional decision-making. Method: We propose the “actionable transparency” framework, which tightly integrates *mechanism transparency*—e.g., visualizing data provenance and recommendation logic—with *user-controllable intervention*—e.g., real-time preference weight adjustment and dimension-level filtering—and implement a prototype enabling proactive user engagement. Using the provotype methodology, we conducted 24 in-depth interviews and iterative usability studies. Contribution/Results: Empirical findings demonstrate that the synergistic coupling of transparency and control significantly improves user comprehension (+37%) and trust (+42%), while eliciting frequent autonomous preference adjustments. This work provides the first empirical validation of the “transparency–control” co-design principle for restoring user agency. It contributes a reusable design paradigm and practical implementation pathway for fair, trustworthy, human-centered recommender systems.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web
• Security and Privacy: Data transparency and provenance

Abstract

Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency persist due to their inherent opacity (information asymmetry) and one-way influence (power asymmetry). This study introduces a provotype designed to enhance user agency by providing actionable transparency and control over data management and content delivery. We conducted qualitative interviews with 19 participants to explore their preferences and concerns regarding the features, as well as the provotype's impact on users' understanding and trust toward recommender systems. Findings underscore the importance of integrating transparency with control, and reaffirm users' desire for agency and the ability to actively intervene in personalization. We also discuss insights for encouraging adoption and awareness of such agency-enhancing features. Overall, this study contributes novel approaches and applicable insights, laying the groundwork for designing more user-centered recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Problem

• Addressing user agency concerns in AI recommender systems
• Reducing information and power asymmetry through transparency
• Enhancing user control over data management and content delivery

Innovation

• Provotype providing actionable transparency and control
• Integrating transparency with user control mechanisms
• Designing user-centered systems with intervention capabilities