A conversation on AI in the hiring space. Sramana Mitra: Let's start by introducing our audience to yourself as well as Cadient. Stuart Nisbet: I am the Chief Data Scientist with Cadient Talent. Our mission is to assist in the area of distributed hourly hiring. My background is in computer science. I spent the majority of my career working in analytics and the application of analytics to a variety of different spaces. In the last ten years, we mostly focused on deep analytics in machine learning, which is referred to as artificial intelligence. We think of it more as augmented intelligence, at least, in our space of hourly hiring. We also focus on some of the deep learning algorithms that try to assist humans in working on things that are uniquely human but can be assisted in terms of how we can apply path knowledge to make better decisions. That's the theme of what we will talk about. I have been in the industry for 33 years now. I graduated in 1987. I focused

Editor's note: Stuart Nisbet is chief data scientist at Cadient Talent, a talent acquisition firm based in Raleigh. RALEIGH -- At Cadient Talent, it's a question that we wrestle with on a daily basis: How do we eliminate bias from the hiring process? The only way to address a problem or bias is to acknowledge it head on, under the scrutiny of scientific examination. Through the application of machine learning, we are able to learn where we have erred in the past, allowing us to make less biased hiring decisions moving forward. When we uncover unconscious bias, or even conscious bias, and educate ourselves to do better based on unbiased machine learning we are able to take the first step toward correcting an identified problem.