Thought Leaders in Artificial Intelligence: Stuart Nisbet, Chief Data Scientist, Cadient Talent (Part 6)

Sramana Mitra: Do you have pointers to open problems and white spaces in this domain?

Stuart Nisbet: We started this conversation on what types of data we have as inputs. We find that a great deal of the data or information that is used in the hiring process does not come through the application or interview process. It comes through more open technologies.

Conversations and employee referral is much stronger than a dot-com application. There is going to be a great opportunity for entrepreneurs looking at technologies that look at unstructured data using natural language processing or even natural language generation to ask questions that come from looking at a résumé. They are quite easy to scan, but they are, in many cases, hard to interpret.

I think some of the white space in what we are doing and also in AI and machine learning in general, is in finding a way to harness all the data when the majority of it is not coming in a structured form through an application with fields, blog posts, social media, interview notes, and conversation chat. That is an area that is right for investment and right for taking off. 

Sramana Mitra: I agree with you. Today, people have so many footprints on social media whether it’s on LinkedIn, Facebook, Twitter, or wherever else. If you can model that footprint, you can get to the whole person at a much deeper level than you would from a résumé. It would give you insights into people in unique ways. The problem is privacy. 

Stuart Nisbet: I’d love to hear your thoughts. For example, if you are hiring for Pet Smart, is it a valid thing to look at an applicant’s Facebook page and look at how many photos they have with pets? Can you determine by looking at their social media if they are an animal lover or not and is that valid?

Sramana Mitra: Those are the kind of signals that are interesting and could be confusing also. You may have heard of Roger McNamee, who is a well-known investor. He was an early investor in Facebook and he wrote this book that was against Facebook – writing about how harmful Facebook has become.

Robert McNamee has a brand. What does that say about him? In the modern era, a younger version of Roger McNamee is a person that you are considering to hire for a position. He probably has a YouTube page or channel.

What signals are you picking up from there and how do you model that signal? What does it say about the person? Is it relevant or not? These are interesting considerations and they are going to be factored into hiring. 

Stuart Nisbet: I think so too. I think transparency, trust, and being able to explain exactly how these decisions were reached, how the models were built, and what variables were used is going to be key. There is a lot of distrust or concern over the misuse of AI and machine learning. 

Sramana Mitra: Thank you for your time.

This segment is part 6 in the series : Thought Leaders in Artificial Intelligence: Stuart Nisbet, Chief Data Scientist, Cadient Talent
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