Negotiating the Shared Agency between Humans & AI in the Recommender System

TL;DR

This paper proposes a dual-control mechanism in recommender systems that enhances user agency by allowing users to manage data collection and influence content personalization, addressing issues of opacity and power asymmetry.

Contribution

Introduces a novel dual-control approach combining transparency and user controls to improve user agency in recommender systems.

Findings

Transparency alone does not increase user agency.

User controls that influence outcomes significantly enhance user agency.

Combining transparency with user controls yields the best user experience.

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.