Written by Nathan Hewitt

Biased people are much harder to fix than algorithms

We worry intensely about the amplification of lies and prejudices from the technology that fuels social media like Facebook, yet do we hold the mirror up to ourselves and check our tendency to hire in our image?

How many times have you told a candidate they didn’t get the job because they were not the right “culture fit”?

The truth is that we humans are inscrutable in a way that algorithms are not, which means we are often not accountable for our biases.

In algorithms, bias is visible, measurable, trackable and fixable.

A compelling feature of our technology is that our AI can’t see you, hear you, and judge you on irrelevant personal characteristics (like gender, age, skin colour) – as a human can. That’s one reason why trusted consumer brands like Qantas, Superdry, and Bunnings use it to make fair unbiased hiring decisions.

To validate that algorithms are bias-free, we do extensive bias testing (impossible to do for humans). We know from this testing that there is no statistical difference between the way the algorithm works on men, women, and people of different ethnicity.

We use these tests for bias testing:

Our bias testing happens at 3 levels:

Score calculated by the predictive model for each candidate.
Recommendation grouping based on score percentile.
Feature values used by the predictive model for training.

For Gender-bias testing:

To analyse whether our test scores have any gender bias we use t-test and effect size. For testing our recommendations of YES, NO and MAYBE groups, we use chi-square, fisher-exact and the 4/5th rule. This last one is the standard test set by the EEOC for any assessment used for candidate selection.

For Ethnicity-bias testing:

We use the 4/5th rule and the ANOVA test.

For Feature-level bias testing

This is to ensure any of the feature values we are using to assess candidate fit are not of themselves biased, we use t-test, effect size and ANOVA test.

Diving into just one of these, using effect size is easy to understand the statistical measurement of the difference in average scores of males and females. If the effect size is positive in our test set, it means females have higher scores than males and vice versa.

The magnitude of the effect size also matters – the larger the magnitude, the more significant the difference is. We generally consider values smaller than +/- 0.3 a negligible difference, values from +/- 0.3 to +/- 0.5 a moderate level difference, and values larger than +/- 0.8 significantly large level of difference.

We periodically test for score and recommendation bias in our models and take action if the bias highlighted is non-negligible. e.g., the effect size is beyond the range of +/- 0.3 or more, we take action- stop the model until we can find the source of the bias and re-train/re-test the new model to make sure the new model is not biased.

For more insight on how our technology removes bias and how we track and measure bias, read diversity hiring


Fairness Comes When You Replace Humans With Machines 

In an earlier blog, we talked about HR’s role in managing business risk. Today we turn our focus on one risk area that occupies CHRO’s, CEOs and Boards- the risk presented by bias and how to maximise fairness by removing bias. 

Despite all the attention generated by International Women’s Day year a few months back and year on year, and myriad other initiatives, Boards, CEO’s and CHRO’s know that bias goes beyond gender and fixing it requires more than a training session or two. 

Humans are full of bias and none of us like admitting that.

Most of us would not even know when are being biased…

‘I just had a feeling he wasn’t going to be any good’

‘he just wasn’t a good culture fit’

‘she just doesn’t have the requisite experience’ 

‘we had such an awesome interview, we could have chatted forever we had so much in common ‘

Bias in recruitment is even less measured and reported on than quality of hire.

It starts with having the data. The data revolution has been happening for decades in every other function but where is the data around recruitment?

  • Do you know if there is bias in screening? hiring? promotion?
  • Do the leaders and Board know those stats?
  • When you run your 5000 graduate applicants through a personality assessment do you probe HR on what the success profile is that is defining screening criteria?
  • Is that profile preferring a certain type (eg over-indexed to qualities like drive, initiative, agility) or does it balance out hiring to include some of those all-important cultural traits like empathy and humility? 
  • Is your hiring profile going to give you more ‘Satya’s’ or more ‘Travis’s’? 

More on bias measurement later…

How can bias be removed from people decisions?

Daniel Kahneman, Psychologist & Nobel Laureate, has this to say about managing bias in human decision-making. 

“When making decisions, think of options as if they were candidates. Break them up into dimensions and evaluate each dimension separately. Then – delay forming an intuition too quickly. Instead, focus on the separate points, and when you have the full profile, then you can develop your intuition.”  

Regarded as the father of behavioural economics, after 5 decades of research he has concluded that the research is unequivocal: When it comes to decision-making, algorithms are superior to people

For further reading on how AI can remove bias in recruitment, download our whitepaper here.

Read Online

What is a good ATS actually supposed to do?

According to Aptitude Research, 58% of us companies are currently dissatisfied with their ATS provider.

One in four are actively looking to replace their tech.

This dissatisfaction comes from a phenomenon known as overstacking.

Put simply, companies over invest in a raft of HR technologies, throwing big dollars into solutions that don’t provide clear benefit or ROI.

The thinking is this: “Everyone has an ATS. If I implement one at my company, I won’t get fired.”

But obviously, as Aptitude’s research shows, most ATSs are not doing what they’re expected to do – they don’t provide enough efficiency, and they don’t solve for things like quality of hire and time to hire; the metrics that CFOs look at very closely.

So what’s the real problem?

Augment your ATS, don’t replace it

The dissatisfaction is due not to an inherent fault in ATS technology, but a fundamental misunderstanding of what an ATS is supposed to do.

Look at it this way: an ATS is like your laptop computer. It has all of the parts that make up a good (or bad) computer: chips, CPUs, keys, a screen, and so on. In this sense, an ATS is more hardware than software. You need it, but you need more, too.

What you add to your ATS – your computer – is what transforms it into a tool that can be used to produce and extract value. If you bought a computer and tried to create documents on it without installing Microsoft Word, for example, you can hardly blame the computer for the missing functionality. It’s not fundamentally designed for word processing – it’s the platform that facilitates it.

Therefore, if your ATS is not helping to improve key performance indicators like quality of hire or time-to-fill, the ATS isn’t the problem. The problem is you don’t have the ATS plug-in designed specifically to satisfy those KPIs.

So, without first considering what a good ATS is, and what a good ATS should do, spending big money to replace it will not solve the problem – only delay it.

How to know if your ATS is salvageable

First, you need to ask yourself (and your business) the following questions:

  • Is your HR tech stack actually providing value, and in a way you can prove?
  • What do you actually need your ATS to do, that it isn’t?
  • What are some better, more cost-effective ways to make your ATS work better for you?

By partnering with your existing ATS,’s smart hiring automation solution can help you achieve 90% completion rates and 90% candidate satisfaction rates – and you can even achieve a time-to-fill of as little as 24 hours.

We’ve even helped one customer, Spark NZ, achieve a near-complete removal of hiring bias.

This isn’t a case of simply throwing good money after bad. It’s about making your ATS into a solution that actually works for you – and in ways that you can prove.

You could replace your tech and call it job done. Maybe you’ll be gone, off to a new business, in the three or four years it will take for the cycle of dissatisfaction to repeat. But that is not a good solution.

Read Online

High volume hiring? Here’s why smart-touch hiring is better than high-touch

The secret to securing great talent is a first-rate candidate experience. If you have been in any way entangled in the aftermath of 2021’s Great Resignation, you know that even an attractive remuneration package, with compelling benefits, is not enough: Now, more than ever, prospective hires will want to see the best of your organisation, and that includes the best of you. You must be fast, decisive, and flexible, from the point of first contact.

This is a problem amplified by scale. If you’re responsible for hiring 100,000 employees per year, for instance, you may find you are required to provide a top-notch candidate experience for that many prospects. You could decide that it is better to do things the old fashioned way, but it is more and more likely that, in doing so, you will miss out on great talent. The cost of such losses is best avoided.

Automation, be it through an assessment tool, conversational Ai platform, or Applicant Tracking System (ATS), is the simple key to solving volume hiring in a chaotic market. However, understandably, many high-volume hiring managers tend to think that automation comes at the cost of personalisation and human contact. If, for instance, you’re processing 5,000 prospects to fit 300 job openings, how do you ensure your candidates are met with the high-touch journey they expect? Is an automated Ai conversation, in the minds of candidates, not just as impersonal as older methods of qualification?

What ‘high-touch’ actually means, and its purpose

On the face of it, ‘high-touch’ implies an emphasis on person-to-person, face-to-face contact in your hiring process. If you can see your candidates, if you can greet them warmly and exalt your free-breakfast policy, you can make them feel special. Sending an email or a link to a form is impersonal, outmoded, and risks alienating the people you want to attract. 

What if, instead, high-touch is a stand-in for meaningful contact, instead of lots of contact? What if you could conduct a smooth, quick, and painless interview process that:

  • Asked the best from candidates, and allowed them to provide thoughtful responses in their own time
  • Explored the human aspects of their personality, and not just the line items on their CV
  • Ignored, by design, the aspects of candidates prone to bias (such as appearance, ethnicity, age, and so on)

Is that not more effective than a by-the-book interview in which you smiled a lot, engaged in forgettable small talk, and discussed a laundry list of perks?

Woolworths, Australia’s largest private employer, adopted the smart-touch automated hiring approach, and won handsomely for it. They used our Chat Interview (chat-based) and Video Interview (video-based) solutions to assess nearly 9,000 candidates, achieving a candidate satisfaction score of 9.2 out of 10. We saved the hiring team time and money, helped give each of their candidates the fairest possible go, and best of all, helped them achieve their hiring targets. 

Woolworths wanted the equivalent of a high-touch candidate experience, and judging by these candidate testimonials, they certainly got it:

“The chat makes you feel like you’re in a safe space – it gives everyone an equal opportunity instead of an in person interview as people can get extremely nervous”.

“I found the process to be reflective and I liked how they wanted to know about me”.

“Everything was amazing! By far the best interview system I’ve encountered! It allowed me to be comfortable and be myself, it really allowed me to take my time with my responses rather than stutter over my words”.

“It was great. I like the potential to retake videos and how quick you’ve responded”.

There you have it: That is how a small hiring team can process nearly 10,000 candidates, using conversational Ai, and offer a truly high-touch candidate experience. But the benefits don’t stop there.

With a smart-touch automation solution, Talent Analytics can heighten your touch

When you entrust your hiring process to Smart Interviewer, our smart interviewer, you automate the process of meaningful data collection. That data is then transformed into actionable insights that help you improve your hiring processes. With TalentInsights, you could learn:

  • Your average time to hire
  • Estimated time saving in hours (incidentally, we’ve helped our customers save a total of 530,000 hours that would have been spent in cumbersome, high-touch interviews)
  • The quality of your candidate pool overall, or by gender
  • Overall candidate engagement scores

And much more. Suddenly, you have the numbers to back your wider hiring strategies, be they focussed on DEI, or fairness, or another goal. You can show your business that you are making real, quantifiable strides, and leading the way in efficiency and social responsibility.

The appetite for good, actionable data in HR is higher than it has ever been. Hiring managers are waking fast to the realities of the Great Resignation – that we just don’t know as much as we should about what constitutes good talent and candidate experience. In other words: We don’t really know why people are leaving, and we don’t really know why they do or don’t choose us in the first place.

According to a recent study by Madeline Laurano, founder of Aptitude Research, only 50% of the companies that invest in Talent Analytics actually trust the source of their data. When you consider that around 80 million American workers are hourly workers, one of the hardest-to-recruit employment segments of the moment, it becomes clear that the need for useful data is absolutely critical. 

What approach will you take? What kind of experience will you provide your candidates, before and after hiring? What kind of data will guide your decisions? Remember: The choice to do nothing is still a choice, and it has an indeterminate cost.

Read Online