On Measuring the Diversity of Organizational Networks

TL;DR

This paper introduces FairEA, a novel algorithm for assigning employees to positions in social networks that maximizes diversity and fitness, providing a benchmark for organizations to evaluate their hiring practices.

Contribution

The paper proposes FairEA, a new algorithm that balances diversity and fitness in employee assignment within social networks, addressing under-representation and segregation issues.

Findings

FairEA effectively finds high-diversity, high-fitness matchings on real and synthetic networks.

FairEA outperforms baseline methods in balancing diversity and organizational fitness.

The algorithm provides a useful benchmark for evaluating hiring and assignment practices.

Abstract

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.