Distributional Inequality Metric

Distributional inequality metrics quantify the differences in data distributions, addressing the limitations of traditional metrics that focus on point-wise comparisons or ignore the overall shape of the distributions. Current research explores these metrics across diverse applications, including evaluating the fairness of machine learning models, assessing the performance of natural language generation systems, and analyzing the impact of recommendation algorithms. This work is significant because it provides more nuanced and robust ways to measure disparities and model performance, leading to improved fairness, accuracy, and understanding of complex systems in various fields.