Erratum: Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

[This corrects the article DOI: 10.1016/j.patter.

Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of machine learning (ML) models amplifying societal biases. In this paper, we propose adapting income inequality metrics from economics to complement existing model-level fairness metrics, which focus on intergroup differences of model performance. In particular, we evaluate their ability to measure disparities between exposures that individuals receive in a production recommendation system, the Twitter algorithmic timeline.