Arena applies predictive analytics and machine learning to solve talent acquisition challenges. Learning algorithms analyze a large amount of data to predict with high levels of accuracy the likelihood of different outcomes occurring, such as someone leaving, being engaged, having excellent attendance, and more. By revealing each individual’s likely outcomes in specific positions, departments, and locations, Arena is transforming the labor market from one based on perception and unconscious bias, to one based on outcomes.
The company leverages techniques known as adversarial networks ( an aspect of Generative Adversarial Networks (GAN’s), tools that pit one algorithm against another) to create an Adversarial Fairness model that removes 92%-99% of latent bias from algorithmic models.
If undetected and unchecked, algorithms can learn, automate, and scale existing human and systemic biases. These models then perpetuate discrimination as they guide decision-makers in selecting people for loans, jobs, criminal investigation, healthcare services, and so much more.
“We succeeded in our intent to reduce bias and diversify the workforce, but what surprised us was the impact this approach had on our core predictions. Data once considered unusable, such as commuting distance, we can now analyze because we’ve removed the potentially-associated protected-class-signal,” says Michael Rosenbaum, Arena’s founder and CEO. “As a result, our predictions are stronger AND we surface a more diverse slate of candidates across multiple spectrums. Our clients can now use their talent acquisition function to really support and lead out front on Diversity and Inclusion.”
Company Type: Startup
Region: US & Canada
Industry Category: Human Resources Data Science
Fighting Type of Bias: Algorithmic bias
Product: AI App for Human Resource Technology
