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Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems

Though recommender systems fundamentally rely on human input and feedback, human-centered research in the RecSys discipline is lacking. When recommender systems aim to treat users more fairly, misinterpreting user objectives could lead to unintentional harm, whether or not fairness is part of the aim. When users seek to understand recommender systems better, a lack of transparency could act as an obstacle for their trust and adoption of the platform. Human-centered machine learning seeks to design systems that understand their users, while simultaneously designing systems that the users can understand. In this work, I propose to explore the intersection of transparency and user-system understanding through three phases of research that will result in a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems.