Evaluating race and sex diversity in the world's largest companies using deep neural networks
This provides a method for rapid, unbiased diversity assessment in large organizations, though it is incremental as it applies existing deep learning techniques to a new dataset.
The study applied deep neural networks to predict race and sex from executive profiles of the world's 500 largest companies, ranking them by diversity indices to assess organizational diversity automatically and impartially.
Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep-learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks.