PredictiveHire CEO Barb Hyman: AI for candidate experience, assessment and hiring bias elimination

Learn how PredictiveHire uses AI to find the right people whilst removing bias from the hiring process.

Yuliya Sychikova
COO @ DataRoot Labs
18 Nov 2019
12 min read
PredictiveHire CEO Barb Hyman: AI for candidate experience, assessment and hiring bias elimination

Barb Hyman is the CEO of PredictiveHire, an online platform which takes a data-first approach to help businesses hire the right people. By leveraging the power of technology, PredictiveHire helps quickly evaluate a large number of applications and narrow down the list to those with suitable profiles and ensuring an easy and engaging candidate experience.

What is PredictiveHire’s vision? What do you hope to accomplish?

Barb Hyman: Our mission is to help people find their dream job and customers to find their dream candidates. We do that by bringing AI to recruitment, a process that has barely changed in 300 years! Hiring today is still based on what you’ve done in the past as a predictor of what you will do in the future. It’s based on who you know and whether or not that’s who I know. In other words - It’s loaded with bias. Imagine if your bank made their loan decisions in the same manual and subjective way that guides most recruitment decisions today!

One of your goals at PredictiveHire is to make sure that that the organizations get the candidate experience right? Why do you pay such importance to the candidate experience? Why now?

Most of our customers are also consumer brands that get loads of applicants and might hire only 2-3%. Every candidate is also a potential customer of their business. That means the candidate experience matters not just for recruitment but for the whole business. Hence, the speed, convenience, and TRUST in the assessment and the process are paramount, which cuts out psych tests, video interviews and probably games as well. What we see with our text-based assessment is a high level of trust and relatability by candidates of all types. It feels very natural and easy to engage via text as we do that every day in our regular lives.

How would you describe your ideal customer that gets the most value from your platform?

A sponsor who can lead the change - as we are Selling Change when introducing a technology based on AI. Someone who gets that tech delivers deliver not just efficiency to recruitment but value to the whole business - better people, the right culture and better business results!

Amid the intensifying war on talent, what advantages does the technology behind the PredictiveHire provide to its clients?

The golden triangle of recruitment is time, quality and cost. All three are dramatically impacted by AI. One of our customers in the travel & tourism sector, was spending 160 hours looking at CVs and watching candidate videos for each hiring campaign. Now they spend two hours, as they take our ranked list of shortlisted candidates, qualified by our AI. That’s 80x faster! Another large customer from retail used to spend the equivalent of $1.3m annually in people time just on screening CVs. Using our tool at the top of the funnel as the 1st interview for every candidate for them reduced the screening time by 100%!

If you are hiring less than 5% of your applicants, you need technology to automate the screening. That will reduce your time and cost-to-hire by 90%. This matters as most HR teams are getting less budget not more.

Some of the other benefits of using this technology include removing bias ...

The machine has a beginners mind, meaning that it doesn’t know the color of your skin, your gender or the school you went to. The only data that goes into our predictive models is text data. No CV data or any PI data. That assures a level playing field for all candidates, giving everyone a fair and equal opportunity.

And the If your CEO really wants to drive home the message that diversity matters, then technology has to be part of the solution. People cannot be trained out of their bias. And all the positive intent in the world won't cut it either. That means relying on clean data to assess people. Not CVs, not tools that are built off your existing employee base (aka the Amazon disaster).

And the learning capability …

Machines are also way more capable of learning, and learning fast if they are fed with meaningful feedback.

And the credibility for HR that comes with having a consistent process grounded in objective data ...

If the business is telling you that HR needs to give them better people, then you need to be able to measure that. Right upfront. Who will be the best person? That means data on every candidate and data on every employee.

Tesla AutoPilot has reached 1 billion miles of driving, in a multitude of conditions on roads around the globe. No single driver or, for that matter, a team of drivers, will ever get that experience in a lifetime, not to mention consistently apply the learnings to give its owners a safe and sound ride experience. This is the power of AI and people are trusting it to take them places.

At PredictiveHire we do the same with screening the right candidates for our clients, fairly and efficiently. What our AI learns from hundreds of thousands of candidates, industry level nuances and post-hire performance is orders of magnitude beyond what a human recruiter will ever learn in a lifetime.

Where does the data come from and how does your algorithms help to make the right business decisions?

We use text data to identify the DNA (defined in the form of predictive features extracted from the text) of the best in a role. We then apply that DNA to assess a candidate via a mobile conversation that takes 20-25 mins. Advances in computational power, machine learning, natural language processing (NLP) enable us to profile personality from text in a few seconds.

How exactly does your company help avoiding the hiring bias?

We don’t use any PI data, such as CV data, in our models - we use only the words that you write. We don’t know anything about your personality making the hiring process truly agnostic of personal profile data. For training, we use is objective performance data such as churn, i.e. how long you were with the business, sales performance - data that comes from an existing system that is a measure of performance that every person gets. We don’t use for instance performance ratings as training data as that is prone to subjectivity and bias.

You overtook leadership of the startup from its founder. What are you most proud of as the CEO?

We have been able to assemble a world-class data science team who are experimenting and creating new IP that in a short time has surpassed IBW Watson in its predictive power. I am especially proud of the fact that we are a small team of 13 and every member of the team acts as a founder, and that we have been able to win and deliver value to some of the most trusted companies in Australia.

What features are up next for your clients? What do you hope to achieve 3-5 years from now as an organization?

Most of our clients use group interviews or assessment centers as an efficient way to assess candidates shortlisted by PredictiveHire. Many of them prior to using us were seeing conversion rates or yields of 30-50%. Now with us doing the 1st interview, those numbers are more like 60-75%. However, these 3-hour marathon interview forums are often run like a process, without a lot of data and the risk of bias is high. Our next product is an app that our customers can use to address these issues - giving data-driven insights on each candidate, data-driven interview questions, and data for calibration that ensures more efficient and less biased decisions.

Sign up to be one of the first to use our beta version!

Now, as you very well know, there is a certain unease among people about AI in general as AI is expected to replace jobs. What do you personally think about it? Is PredictiveHire contributing to that notion?

People said that about the travel sector when marketplaces like Webjet, Expedia came online, but, at least, in Australia, the largest travel agency Flight Centre is booming! AI takes away the dull, monotonous work,liberating people from those tasks. That means they can do more interesting and value-added work that only humans can do. For example, selling candidates on the role and the organization, acting as business partners to the hiring manager to ensure they have real clarity on the role requirements, thinking creatively and laterally about the kind of talent that would be best suited to the role and proactively sourcing that talent vs “post and pray”. Automation has been liberating humans of uninspiring work for decades - AI is just an extension of that in the workplace.

Automation has been liberating humans of uninspiring work for decades - our AI is just an extension of that in the workplace.
Barb Hyman
CEO @ PredictiveHire

HR Tech has undergone quite a few transformations. Now we see the impact of AI. What do you think is next for the industry? What, in your opinion, will we see in the next 5 years?

Hopefully, lots of consolidation. There is so much new technology and a fair amount is at the margin of the value chain. HRDs are choosing “one size fits all” options like Workday but then discovering they don’t meet all their needs. Best of breed is tough to manage from a management and integration point of view. So, the tech companies need to consolidate along the value chain to make it easier for customers to get the best product for every piece of the puzzle.

What are the key challenges facing HR tech industry to make those leaps?

Invest first and second in those tools that will drive the most sustained business value. That’s not, in my humble opinion, engagement survey tools or reward programs. Rather, they are the tools that ensure consistency, fairness, and quality of your most important people decisions - who you hire and who you promote. If you have the right people, a lot of the HR work takes care of itself.

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Yuliya Sychikova
Yuliya Sychikova
COO @ DataRoot Labs
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Yuliya Sychikova
COO @ DataRoot Labs
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.


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CEO @ DataRoot Labs
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