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This paper highlights our ongoing efforts to create effective informationcurator recommendation models that can be personalized for individual users,while maintaining important fairness properties. Concretely, we introduce theproblem of information curator recommendation, provide a high-level overview ofa fairness-aware recommender, and introduce some preliminary experimentalevidence over a real-world Twitter dataset. We conclude with some thoughts onfuture directions.

Fairness-Aware Recommendation of Information Curators