# Diversity and Inclusion Index with Networks and Similarity: Analysis and its Application

arXiv: https://arxiv.org/abs/2410.05668 · HTML: https://mlsanity.com/paper/2410.05668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Diversity in Machine Learning](https://mlsanity.com/paper/1807.01477/) (2018)
- [Diversity and Inclusion Metrics in Subset Selection](https://mlsanity.com/paper/2002.03256/) (2020)
- [Measuring Diversity in Heterogeneous Information Networks](https://mlsanity.com/paper/2001.01296/) (2020)

## Abstract (arXiv)

In recent years, the concepts of ``diversity'' and ``inclusion'' have attracted considerable attention across a range of fields, encompassing both social and biological disciplines. To fully understand these concepts, it is critical to not only examine the number of categories but also the similarities and relationships among them. In this study, I introduce a novel index for diversity and inclusion that considers similarities and network connections. I analyzed the properties of these indices and investigated their mathematical relationships using established measures of diversity and networks. Moreover, I developed a methodology for estimating similarities based on the utility of diversity. I also created a method for visualizing proportions, similarities, and network connections. Finally, I evaluated the correlation with external metrics using real-world data, confirming that both the proposed indices and our index can be effectively utilized. This study contributes to a more nuanced understanding of diversity and inclusion analysis.