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Diversity is one of the fundamental properties for the survival of species,populations, and organizations. Recent advances in deep learning allow for therapid and automatic assessment of organizational diversity and possiblediscrimination by race, sex, age and other parameters. Automating the processof assessing the organizational diversity using the deep neural networks andeliminating the human factor may provide a set of real-time unbiased reports toall stakeholders. In this pilot study we applied the deep-learned predictors ofrace and sex to the executive management and board member profiles of the 500largest companies from the 2016 Forbes Global 2000 list and compared thepredicted ratios to the ratios within each company's country of origin andranked them by the sex-, age- and race- diversity index (DI). While the studyhas many limitations and no claims are being made concerning the individualcompanies, it demonstrates a method for the rapid and impartial assessment oforganizational diversity using deep neural networks.

Evaluating race and sex diversity in the world's largest companies using deep neural networks