  Machine learning predictors have been increasingly applied in production
settings, including in one of the world's largest hiring platforms, Hired, to
provide a better candidate and recruiter experience. The ability to provide
actionable feedback is desirable for candidates to improve their chances of
achieving success in the marketplace. Until recently, however, methods aimed at
providing actionable feedback have been limited in terms of realism and
latency. In this work, we demonstrate how, by applying a newly introduced
method based on Generative Adversarial Networks (GANs), we are able to overcome
these limitations and provide actionable feedback in real-time to candidates in
production settings. Our experimental results highlight the significant
benefits of utilizing a GAN-based approach on our dataset relative to two other
state-of-the-art approaches (including over 1000x latency gains). We also
illustrate the potential impact of this approach in detail on two real
candidate profile examples.
