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

arXiv: https://arxiv.org/abs/2403.15919 · HTML: https://mlsanity.com/paper/2403.15919/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2024.

## Built on by (most cited first, 5 of 5)

- [Interaction-Driven Browsing: A Human-in-the-Loop Conceptual Framework Informed by Human Web Browsing for Browser-Using Agents](https://mlsanity.com/paper/2509.12049/) (2025)
- [Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems](https://mlsanity.com/paper/2604.10029/) (2026)
- [Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits](https://mlsanity.com/paper/2508.02328/) (2025)
- [Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms](https://mlsanity.com/paper/2508.13440/) (2025)
- [Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice](https://mlsanity.com/paper/2512.05397/) (2025)

## Abstract (arXiv)

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.