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Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces

Mengke Wu, Weizi Liu, Yanyun Wang, Weiyu Ding, Mike Yao

TL;DR

A provotype is developed that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes that reaffirm users'desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Abstract

AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes. The walkthroughs and interviews with 19 participants show how these features help users interpret personalization signals, understand how their actions influence outcomes, address concerns from unwanted inference to narrow feeds (e.g., filter bubbles), and build trust in the system. We also identify strategies for promoting adoption and awareness of agency-enhancing features. Overall, our findings reaffirm users' desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces

TL;DR

A provotype is developed that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes that reaffirm users'desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Abstract

AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes. The walkthroughs and interviews with 19 participants show how these features help users interpret personalization signals, understand how their actions influence outcomes, address concerns from unwanted inference to narrow feeds (e.g., filter bubbles), and build trust in the system. We also identify strategies for promoting adoption and awareness of agency-enhancing features. Overall, our findings reaffirm users' desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Paper Structure

This paper contains 57 sections, 8 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Information Asymmetry
  4. Power Asymmetry
  5. Existing User Control in RS
  6. Filter Bubbles & Preference Misalignment
  7. Mitigation, Limitation, and Implication
  8. Information Fairness
  9. PROVOTYPE DESIGN
  10. What is a Provotype and Why We Use It
  11. Ideation Process
  12. Proposed Features in the Provotype
  13. Data Management
  14. Content Discovery
  15. Personal Modes
  16. ...and 42 more sections

Figures (8)

  • Figure 1: Examples of Different Types of Agency. Perceptual Agency (Left): (a) Source Transparency, (b) Process Transparency; Behavioral Agency (Right): (c) Executing Permissions, (d) Specifying Preferences, (e) Providing Feedback.
  • Figure 2: Feature Mapping by Focus and Placement.
  • Figure 3: Data Management Suite: 1) Data Type Weighting, 2) Data Usage % Indicator, 3) Data Aging Setting, 4) Data Diary.
  • Figure 4: Content Discovery Suite: 1) Adventure Content (A - Balance, B - Time), 2) Disruption Content (A - Frequency, B - Topic, C - Time), 3) Content Tagging.
  • Figure 5: Personal Modes: 1) Enrich Current Settings into a Mode, 2) Choose from Pre-Configured Modes, 3) Mode Switcher.
  • ...and 3 more figures