CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations

This project will test the hypothesis that prioritizing content that generates engagement in diverse audiences will improve the trustworthiness of recommendations. It will explore the relevant dimensions under which the heterogeneity of an audience provides a good signal for news quality. The technical contributions will include a set of new regularization techniques to incorporate audience diversity of news sources into content recommendation methods, a quantitative evaluation of the effect of different re-ranking methods on the quality of the information diet of news consumers, especially older consumers, and a new experimental methodology on counterfactual ranking with high ecological validity.

1/1/2023 - 12/31/2027

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