# On Measuring the Diversity of Organizational Networks

arXiv: https://arxiv.org/abs/2105.06929 · HTML: https://mlsanity.com/paper/2105.06929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 2 of 2)

- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics](https://mlsanity.com/paper/1807.00553/) (2018)

## Abstract (arXiv)

The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diver- sification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.