Level: Intermediate Robert Reul, Founder, Isometric Solutions
Eliminating discrimination in the workplace. Possibly the one goal shared by applicants and employers is the ability to seek and secure employment on a full set of non-discriminatory characteristics: knowledge, skill and experience. The unfortunate reality is that these are all too often trumped by other more easily discernable factors that have very little to do with job performance, and worse, embody unethical and illegal practices when hiring applicants for a job. Can data analytics be used to exclude discriminatory bias during the hiring decision? Using extensive academic literature on personality and performance, several frameworks emerge that serve as outcome vectors for predictive models, namely the “Big 5” personality traits and trustworthiness, likeability and confidence. But what to do about the predictors? Experimentation with facial image processing data from sources such as Google and Microsoft showed predictive promise. By using a series of analytic methods that screen for predictive potential and reduce dimensional complexity, predictive prowess emerges with a completely non-discriminatory set of latent variables that inform the “whom to interview”/ “whom to hire” recommendations based purely on cultural personality fit. This presentation will reveal the challenges and successes of this effort, proving it’s not only feasible – it may become the new normal.