When I was leading the People & Culture team at the REA Group, my new CEO was passionate about Values, and the central role they play in defining your culture. Following a successful change program to evolve new Values that mirrored the desired Culture, one that would set the business up for continued growth and as a talent magnet, she asked me how we were going to embed those Values through our people processes – who we hire, who we promote, who we reward etc.

It couldn’t be a screen saver pop up or posters on a wall. The values had to be really heard and felt. At the same time, we also had a business that was hiring in the hundreds each year so scaling culture means getting this right.

These are two distinct notions when it comes to hiring: hiring for values and for culture.

One should stay pretty fixed, and the other should be dynamic as your business context is always changing. If a company’s values are its bedrock, then a company’s culture is the shifting landscape on top of it. Hiring purely for culture is a recipe for self-reinforcing hiring, aka hiring that is biased. As we all know, innovation comes from diversity of background/thought/etc, so by hiring only for culture you can decrease, or even stifle innovation.

Celebrate that just as your product is always evolving, so will your culture. That means people who were great when you were a team of 50 may not be the right person for when you get to 500.

At Sapia we work with our customers to ensure their values are embedded right from the first interview.

This takes many forms, including:

• Use the language of our customers when we are configuring the interview questions. From ‘team’ to ‘crew’ or ‘family, we use your language to build rapport with candidates
• Ask questions that specifically talk to your Values. For example, safety is paramount for our airline and FMCG customers. We ask questions to gauge awareness of safety risks, such as “Drawing on your own experience, how would you make sure everyone in our store – our customers and your team members – are safe?”
• Learning from every person who joins or leaves the business. For everyone we work with, we know who sticks around in the role and who doesn’t. This will generally be either because they weren’t the right fit and they self-selected out, or the business made a decision to exit them for behavioural reasons. Taking that performance data and using it to refine the benchmark for future hiring means every candidate recommended after using Sapia as your 1st interview is a better Values fit than the last one.

And that’s why machine learning is the holy grail of smarter hiring. No recruiter could ever get that feedback data at the scale and speed to improve their recruitment process. But using Sapia we make a hard decision easier, meaning you can focus on hiring the right people to grow your business, at scale, without sacrificing the candidate experience. And if the VP for a global business focused on connecting people to opportunity can’t recognise bias, it’s a sure sign we need to pay more attention to who, and how, we hire.

“Talent is really distributed very evenly in the world, and opportunity is not.”

So, what do you think? Is your hiring values-driven, or based on the ever-intangible ‘culture-fit’? How do you scale hiring based on values? And how can we in HR, Talent Acquisition and Recruitment support hiring managers to grow innovative, diverse teams?

In recent years, we have all wisened up to the risk of using CVs to assess talent. A CV as a data source is well known to amplify the unconscious biases we have. A highly referenced study from 2003 called “Are Emily and Greg More Employable than Lakisha and Jamal?” found that white names receive 50 per cent more callbacks for interviews.

However, during COVID, we reverted to old ways in a different guise. 

HR substituted CV as a data input with video interviews. 

This isn’t a step forward.

Video hiring productises bias. It actually enables bias at scale.

It leads to mirror hiring – those who look and sound most like me. Instead of screening CVs in 30 seconds now, your team is watching 3-minute videos, so recruiting takes longer, and it’s exhausting.

Video platforms are being challenged in the US (EPIC Files Complaint with FTC about Employment Screening Firm HireVue) for concerns over invisible biases that may be affecting candidate fairness given the opaque nature of those algorithms. Facial recognition systems are worse at identifying the gender of women and people of colour than at classifying male, white faces. This year IBM openly pulled out of facial recognition, fearing racial profiling and discriminatory use, partly due to the questionable performance of the underlying AI.

How did we substitute one inferior and biased methodology with another that’s arguably even more biased? 

We get that at some point you and the candidate need to meet, although no rule says you need to see someone to hire them. That’s just a bias (much like the bias pre-Covid) that you need to see someone at work to know that they are doing the work. 

Blind hiring means you are interviewing a candidate without seeing them or knowing what school they went to, the jobs they have had. It’s a real meritocracy in that it’s fair for the candidate – and also smart for your organisation. 

If you are hanging your hat on the fact you just finished bias training- research has shown consistently unconscious bias training does not work.  

While we have all been dutifully attending it for years, the truth is the change factor is zero. 

At a recent event attended by academics and data-loving professionals –whilst there was a welcome recognition that humans are more biased than Ai, and despite hearing that Wikipedia lists more than 150 biases we humans have – the majority of the audience still believe the impossible: that we can be trained out of our unconscious biases. 

Algorithms are better at dealing with biases

The Nobel Prize winner Dr Daniel Kahneman prescribes an algorithmic approach as better at decision-making to remove unconscious biases. He claims “Algorithms are noise-free. People are not. When you put some data in front of an algorithm, you will always get the same response at the other end.”  Also, see why machines are a great assistive tool in making hiring a fair process, here. 

We know your inbox is flooded with Ai tools with each proclaiming to remove bias and give you amazing results and it’s tough to discriminate between what’s puffery, what’s real and what you can trust. 

 If your role requires you to know the difference between puffery and science, then read this. Buyers Guide: 8 Questions You Must Ask.

The 30-second due-diligence test that every HR professional should be asking when presented with one of these whizz-bang Ai tools is this:

• No data scientists in the team = not likely to be based on Ai
• No research available even under NDA to substantiate the method of assessment being used = pseudoscience or science that’s flawed if the company is not prepared to share it 
• No regular bias testing to review = the Ai is likely to be biased in application 
• Data used to training the models is 3rd party/ social media data = high risk of bias. 

 It’s critical, in fact, it’s a duty of care you have to your candidates and your organisation to be curious and investigate deeply the technology you are bringing into the organisation. 

We have to be careful not to think that all AI is biased. AI is based on data, and that data can be tested for bias. ‘Data-driven’ also means transparent. Testing for bias, fairness and explainability of AI models is an active area of research and has advanced a lot. If built with best practices, AI can be used to challenge human decisions and interrupt potential biases. In the end, hiring is a human activity, and the final outcome should always be owned by a human.    

If you want to know more about the research that defines the Sapia approach, look here. 

If you want to know more about our bias testing, look here. 

Have you seen the Inclusive e-Book?

Making inclusion an HR, not a PR priority.

It offers a pathway to fairer hiring in 2021. In this Inclusivity e-Book, you’ll learn: 

• How to design an inclusive recruitment path. From discovery to offer and validation of the process.
• The hidden inclusion challenges that are holding your organisation back.
• How to tell if Ai technology is ethical.

Download Inclusivity Hiring e-Book Here >

Get diversity and inclusion right whilst hiring on time and on budget.

In case you missed it: The other week, Sage CEO, Stephen Kelly, sat down with Sapia’s Steven John, number 45 on the Sage Top 100 list for 2017, and asked him his thoughts on AI and just how important he feels it is to get it right.

Stephen Kelly: “I’m really pleased to be engaging today with Steven John… an absolute expert in recruitment and people development.”

“So, Steven, tell us a bit about what you’ve done. You’ve got a massive following, 18,000 followers on Twitter. You’re a renowned expert. Tell us a bit about that.”

Steven John: “Well, my background is technology and recruitment. As a lot of recruiters do, I kind of fell out of university with no real idea of a career path and landed in tech recruitment. I spent 13 years as a recruiter and then more recently had the opportunity to take my professional skills that I learned in recruitment and bring them into a business that’s using AI to help businesses make smarter hires.

“I’m a customer success manager for an AI business called Sapia.

“In terms of what we do, we use predictive models to help businesses make smarter hires so they can identify who might be a better or more likely to be a better salesperson or deliver a better customer service experience to their customers.

“Whatever the metrics or the KPIs that their business is using to understand how its people are operating, our solution can help you understand, from the candidate market, who should we be spending our time with, who should the human beings within our talent team be spending time talking to.

“Because of the model, the algorithm has helped us sift through quite a large number of candidates. I’m sure you guys get hundreds of thousands of candidates here. So how do we identify those shiny pins in the haystack? So that’s what our models do.”

Smarter hiring using AI

SK: “Well, I think that’s brilliant, Steven. And obviously, kind of the relevance and gems of this Facebook Live session is to bring it down to all the entrepreneurs out there who are thinking about growing their business, living their dreams, pursuing their passions, and we all know the fuel of that is talented people.

“You mentioned artificial intelligence – AI, machine learning, predictive analytics aimed to make smarter hiring decisions that will really boost your business forward. What is your current experience of where we are on that journey?”

SJ: “I think a lot of businesses are ready. I think more businesses are ready than they probably realise. If I think about the numerous engagements that I’ve had with numbers of businesses, prospects and current clients, the things that strike me as quite interesting are the amount of data that businesses have.

“Surprisingly, some of the businesses who I would have thought would be incredibly data-heavy, will have a lot of data on their people, haven’t been quite so. But the good news for those businesses and even the smaller businesses is that there are solutions available in the market that can help many companies get started on that journey.

“Sometimes I am surprised by how other businesses or some businesses invest their time, money and effort in technology solutions, in buildings, in lots of infrastructure and pieces of kit. But what they don’t necessarily do is invest as much money in their people.

“The encouraging thing is there are now lots of solutions available to businesses of all shapes and descriptions that will really help them start to make smarter decisions for their hiring processes.

“The people are the lifeline of the company. The cost of people is probably one of the most.”

SK: “It’s worth noting most of our customers who are in the services business, about 70% to 80% of their cost base is the people they hire and manage. And we believe in people science.

“So certainly, when they’re here, we want to be pretty scientific, but the recruitment of them could be as scientific as that so we get the right person with the right skills, the right attitude, and the right competence to be successful.”

Sage Advice: How AI and Smarter Hiring Can Boost Your Business

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