Reference
Wu, M., Liu, W., Wang, Y., & Yao, M. (2025). Negotiating the Shared Agency between Humans & AI in the Recommender System. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA ’25, 1–9. https://doi.org/10.1145/3706599.3719900
Abstract
Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency arise from the algorithms’ inherent opacity (information asymmetry) and one-way output (power asymmetry). This study introduces a dual-control mechanism aimed at enhancing user agency, empowering users to manage both data collection and, novelly, the degree of algorithmically tailored content they receive. In a between-subject experiment with 161 participants, we evaluated the impact of varying levels of transparency and control on user experience. Results show that transparency alone is insufficient to foster a sense of agency, and may even exacerbate disempowerment compared to displaying outcomes directly. Conversely, combining transparency with user controls—particularly those allowing direct influence on outcomes—significantly enhances user agency. This research provides a proof-of-concept for a novel approach and lays the groundwork for designing more user-centered recommender systems that emphasize user autonomy and fairness in AI-driven content delivery.
Highlights
Summary
The argument in my own words, as full paragraphs.
Critical analysis
| Strengths | Weaknesses |
|---|---|
What I disagree with, and why.
Relevance to my research
How it shapes my thinking or design.
Key terms
Links to glossary notes, e.g. friction.
Connections
Links to related anchors.