Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics. (arXiv:2202.01615v1 [cs.CY])

The harmful impacts of algorithmic decision systems have recently come into
focus, with many examples of systems such as machine learning (ML) models
amplifying existing societal biases. Most metrics attempting to quantify
disparities resulting from ML algorithms focus on differences between groups,
dividing users based on demographic identities and comparing model performance
or overall outcomes between these groups. However, in industry settings, such
information is often not available, and inferring these characteristics carries
its own risks and biases. Moreover, typical …

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