Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems

Researchers conducted interviews with 11 practitioners at major tech companies to study how fairness considerations are integrated into recommender system workflows. The study identified key challenges including defining fairness in RS contexts, balancing stakeholder interests, and facilitating cross-team communication between technical, legal, and fairness teams.

• →Large tech companies face significant challenges in translating academic fairness research into practical recommender system implementations.
• →Key technical challenges include defining fairness in RS contexts, balancing multi-stakeholder interests, and adapting to dynamic environments.
• →Organizational barriers include insufficient time allocation for fairness work and poor cross-team communication.
• →The study provides actionable recommendations for both RS practitioners and HCI researchers to improve fairness integration.
• →Semi-structured interviews with 11 practitioners revealed gaps between academic theory and industry practice in bias mitigation.