Written by Nathan Hewitt

If you think humans can hire better without technology, you should read this.

Rarely is hiring somebody a single decision, but one made from a number of smaller decisions along a journey to a final one. As recruitment has become more sophisticated as an industry, so has our understanding of what can be flawed about the decisions humans make including the bias and subjectivity we bring when screening and interviewing candidates. These are essentially human traits that even the most well-intentioned of us cannot escape. 

This does not mean we have to eliminate humans from hiring decisions to make it fairer – that would be problematic too – but rather that we have to use technology at strategic moments in hiring to improve our decision making. Our tendency to be biased is often related to the pressure we are under to make faster decisions. Again, this is human. When looking at thousands of CVs for example, our brains create shortcuts for us to process information that, quite frankly, we are unable to absorb. So we start scanning things based on our own biases in an unconscious way picking out schools that appeal to us, experiences that sound similar, names that feel familiar and people who ‘seem’ like others that we know. 

Predictive tools that parse and score CVs, and help hiring managers assess potential candidates are unfortunately not helpful here, because they too, learn from us to favour certain characteristics that we do from CV data. Ultimately using CV data replicates institutional and historical biases, amplifying disadvantages lurking in data points like what university was attended, what gender someone is, how old they are or even what recreational clubs they belong to. A well publicised example of this was when Amazon tried to build a recruiting engine based on observing patterns in resumes submitted to the company over a 10-year period. Most of them were men, a reflection of male dominance across the tech industry. The result: the input data informed the machine learning that it didn’t like women. 

The better approach is to use objective data and bias mitigating technology at the right moments in a recruiting process. It’s a way of letting the algorithms do the hard work of delving into the details that humans miss when making decisions under time pressure using biased mental shortcuts. This way we can build better accuracy than if humans alone were making decisions on their own, particularly in the early decision making or top of the funnel recruiting, with much higher efficiency given the speed of algorithms. We still need to test constantly for bias in these hiring algorithms, but by utilising them at the right moment we can help hiring managers make better – more human – decisions.

“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 an intuition.”

Daniel Kahneman
Psychologist & Nobel Laureate[1]

How do we help humans make better hiring decisions at Sapia?

  1. We use objective data

    The ability to assess someone’s suitability to do a job is not made using CV data, but rather from information we gather from answering five open-ended questions via a text chat that is ‘blind’ i.e. no identifying information is given to the hiring manager.  In this model everyone gets an interview. Using advanced Natural Language Processing (NLP), we can determine a lot about someone from analysing their text answers. While a standard Myers-Briggs assessment identifies 16 personality types, based on essentially  answering repeated questions, this new way of looking at language can account for 400+ personality types and counting. There is no way a human brain could distinguish these differences in people. This means we can truly identify job fit for all the candidates we screen – without bias –  based on what hiring managers have identified as the skills deemed necessary in their ideal candidates. These skills and abilities cannot be uncovered in any other way.

    See our product in action here.

  2. We constantly test for bias

    Being aware that bias can exist in any data is not enough, you need to constantly test your algorithms for any emerging patterns that mimic human bias. Using a number of tests we are continually looking at our results to make sure that we are not amplifying bias in any way. Our results have shown that it is possible to mitigate bias using algorithms for better hiring outcomes. A recent piece of research looking at the hiring of Aboriginal and Torres Strait Islander peoples, the Indigenous peoples of Australia showed that we can elevate marginalised groups. Other research we have done has also proved we create a fair outcome for people who have English as a Second Language

    See our approach to Ai here

  3. We help you calibrate team hiring decisions

    Ultimately, final hiring decisions do fall back on humans, but this is also where technology can also be used to guide and calibrate scoring that hiring managers make when interviewing candidates. Decisions backed by data minimises the risk of bias, making hiring conversations more robust, and less subjective. Using standardised scoring that is live, the  impression a candidate makes on a hiring manager is ranked against other assessors, as the interview is being conducted. It’s not about replacing human decision makers, but elevating their ability to make smarter, more transparent decisions, we cannot make without the help of technology.

    See how we can help humans interview. 
  4. Continuous learning via feedbackHuman decision making is unscalable. The more people you add to scale decisions, the more inconsistencies and biases you will be adding to the process. Moreover, humans are limited in their capacity to learn from objective feedback data such as which profiles of people work well in a given environment. This is where data-driven approaches like machine learning are far superior. Machine learning models are able to learn continuously from large amounts of feedback data, which candidate profiles are more likely to succeed than others. This ability to retain knowledge and then be able to explain how it arrives at a decision helps organisations to truly learn from their bad hires and keep nudging the hiring outcomes towards growth. Working together, recruiters and hiring managers can benefit from the learnings of AI in challenging their views and making the right hiring decisions.
  1. An interaction that is familiarText chat is how we truly communicate asynchronously,  i.e. on your own time – we all do it everyday with our friends and family. It needs no acting; It is blind to how you look and sound. We all know how to chat. Candidates feel comfortable using chat, as they are in a familiar setting, unlike playing a neuroscience game, a one-way video recording or a psychometric test etc which are unfamiliar or artificial experiences. Many don’t enjoy them as they are made to behave in ways they usually don’t. This high engagement, which we capture via post interview feedback, is also a driving factor in capturing authentic data as candidate’s reflect and express in their own way.


We cover this and so much more in our report: Hiring for Equality.

Download the report here.



Recruitment metrics: Discover what is actually attracting candidates through attribution

Recruitment marketing attribution | Sapia Ai recruitment software

Let’s begin with the obvious: Good talent is in high demand and short supply. Candidates have become discerning shoppers, more aware of their worth than in recent market cycles. 

As a result, the onus is on us to change the way we source candidates and generate demand for our company. It’s no longer a case of boosting job ads across a few different channels; to court the best people, we need to focus on strategies that build meaningful and beneficial connections over the longer term. Today, branding, Employee Value Proposition (EVP), messaging, positioning, and creative differentiation are more important than ever.

Here are some questions you and your team may be asking:

  • How do we best promote our business as the one to work for?
  • How strong is our brand? 
  • What is our content strategy (Or: What do we say, when/how/why and to whom do we say it)?
  • How do we revitalize our Employee Value Proposition (EVP)?
  • How do we reach the best candidates in new and memorable ways?

The essence of new-school recruitment marketing

Summarized in a single phrase, your best recruitment marketing strategy is this: Add value. Sounds simple, but it does need some unpacking.

Take this recent episode of’s Pink Squirrels! podcast, in which we spoke with Jennifer Paxton, VP of People at Jen has taken an always-on approach to talent acquisition by being active as a content creator on LinkedIn. Jen regularly posts helpful tidbits and articles about people leadership, employee engagement, career development, and plenty of other topics. In doing so, she is also able to organically (and indirectly) promote the virtues of 

Here’s what’s neat about this: Jen is promoting Smile whether she references Smile or not. If you’ve built a dedicated audience, and that audience sees your other associations, they are much more likely to look favourably on those associations than if you mentioned them overtly or if they came upon the association in a different context (e.g., a display ad on LinkedIn). That’s good marketing.

According to Jen, this has been a big success for Smile, because she is constantly engaged in the process of creating and fostering good relationships with potential employees. Today, they may simply be followers and consumers of her content; tomorrow, they may be teammates. When a vacancy opens up, Jen has more tactics up her sleeve than merely boosting job ads. Her first (and best) option is to put a call out to her always-growing network of engaged professionals.

What Jen does is not necessarily easy – it requires dedication and consistency – but it is simple. It’s about adding value as a people leader, and creating a first-hand connection with the market. Everyday customer facetime is truly invaluable, and for Jen, it’s certainly working.

If you want to learn more about how you can lead recruitment marketing through an always-on content strategy, you can also check out this Pink Squirrels! episode with Russell Ayles, a veteran recruiter and LinkedIn Top Voice for 2022.

The challenge of recruitment marketing attribution

Here’s the rub: If you’re having to do all these new things over a long period of time to prime and court the talent you want, how do you know what’s working? For example, if it takes six months, at minimum, to build and execute a recruitment content strategy, how will you know in month two or month four how things are tracking?

Trickier still, when your CEO or CHRO asks to report on outcomes, what will you tell them? What level of analysis is suitable for stakeholders at that level? How do you reconcile the need for patience with the performance pressures of the executive?

This conundrum is the main reason most companies don’t bother with an add-value strategy, even when their talent pools have dried into a puddle. After all, the ‘boost-your-job-ad’ method still yields concrete and easily-understandable numbers, even if those numbers are bad.

Going new-school with recruitment marketing requires a bit of faith, supplemented by regular analysis of the signals of success. So let’s look at one of the biggest signals for success: Self-reported attribution.

Ask your candidates: How did you hear about us?

Seems far too simple to be useful, doesn’t it? In actuality, this one question can inform the success and evolution of your entire recruitment marketing strategy. It’s not a quantitative metric, of course, not as black-and-white as your abandonment rate or NPS metrics, but the insights can be truly transformative. Here’s how it works.

For the sake of simplicity, let’s say your team has decided on a three-pronged strategy:

  • All talent acquisition leaders will start posting on LinkedIn, three times a week, according to a pre-set content plan.
  • The Head of Talent Acquisition/CHRO, the CEO, and the company’s marketing leader will create a twice-monthly podcast on an area of interest related/adjacent to the business (for e.g., if you’re a retail fashion brand, you might consider a podcast on the principles of design, or merchandising, or upcycling).
  • The regional Community Manager will take tidbits from the podcast and post them on Twitter daily, alongside a bunch of other fun and light content suited to the medium.

Three tactics, three different channels. Now, to track the ongoing health of these measures, you might look at the following metrics on a monthly basis:

  • Trending traffic to website/careers page
  • Increase in social media followers
  • Increase in podcast shares

And plenty others besides. But, crucially, you should also add a field to the form you use as a first step in a job application: A free-text field with a simple, mandatory question: How did you hear about us?

(Ensure that, in form design, you don’t lead the candidate in any way. Don’t have any pre-text in the field (saying something like ‘e.g., Seek’). You want unbiased results.)

You’ll be amazed at what you can learn. Some candidates will offer you vague and unhelpful responses (like ‘Internet’), but over the medium term, you should start to see trends emerge. For example, if a great many of your good candidates are hearing about you through the podcast, they will tell you, and you will come away with hard numbers showing which of your long-term brand-building strategies is working best.

After six months, you’ll start to see more candidates. And you’ll see the following (for e.g.):

  • 30% of candidates say they heard about you through LinkedIn
  • 40% will say they heard about you through the podcast
  • 10% will say Twitter
  • 20% might cite some other channel, like referrals

This kind of recruitment marketing attribution is helpful because it is simple, it is highly indicative (both of past performance and future improvements), and it is compatible with the reality of the market we’re in. Right now, the majority of candidates aren’t looking for work with you – but they are looking for useful, valuable, enjoyable content. It may be a six-month journey from awareness to application readiness, and you should be with them along that journey, helping, educating, informing.

If, instead, you get stuck looking at the ROI of job ad boostings, or even the success of individual pieces of content, you’ll be led astray by the data. In isolation, individual customer touchpoints do not help you iterate. In fact, they will have you doing something different every week. You’ll confuse your audience, see limited success, get frustrated, and quit.

Channels, conversely, paint a picture of customer consumption behaviors and traffic patterns. They show, over time, that your presence is of net benefit.

The best part about self-reported attribution? You can start doing it now, without making any changes, and start to capture data about your activity and brand strength to date.

Give it a try.

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When unconscious bias becomes a conscious bias

unconscious bias

A perfect example of unconscious bias manifesting in a conscious and damaging way.

At Sapia we are attuned to research and stories around bias – for most of us, it’s the reason we work here.

Our team has observed the speed with which the blame for Coronavirus has targeted an entire ethnicity.

In this case, I’ve heard some say, “it’s not racism, people are genuinely scared of the spread of the virus. It’s a deadly virus. As it originated in China people naturally worry about anyone from China”.

Unfortunately, this is the very definition of bias.

A flawed logic that seems sensible on the surface, nothing but pure stereotyping underneath. Simply, everyone who looks Chinese are not recent travels from China.

Australia is home to 1.2mil Chinese origin Australians according to the 2016 Census. Should we worry about all of them? Bias has no place in fighting any problem, even when it is a deadly virus. It only creates stress and disharmony.

The irony is this: the virus is a true fair operator. It has no racial preferences.  

At the beginning of this week, one of our team who had come down with a cold shared he would work from home, to keep the team safe from his contagion.

We laughed at the time about him being a carrier of Coronavirus.  By the end of the week, members of our team with holidays booked to visit family and travel in China during the Easter break had cancelled their trip.

They did this before Qantas stopped their direct flights and before the Australian government announced that Chinese people won’t be allowed back into Australia.

The team member who had a cold this week is Sri Lankan by birth. I guess that means we would have all been safe if he turned up to work as he is the ‘right’ ethnicity.

We are on a mission: To solve issues of bias in hiring

As a white immigrant myself, I don’t experience those prejudices. I have had career and life opportunities beyond my dreams, unfettered by racial bias.

Building a technology that gives equivalence to such career opportunities is why we work for our company. Some of our team have been screened out of job openings. Maybe they had the wrong name, went to the wrong school or just didn’t look like a cultural fit?

Unfortunately, AI hiring tools can be biased 

Not all AI is equal. HireVue, an AI-driven recruitment company, has recently been taken to the US Federal Trade Commission with a prominent rights group claiming unfair and deceptive trade practices in HireVue’s use of face-scanning technology to assess job candidates’ “employability.”

Using video is an obvious problem as a data source for reasons around race and gender and their associated biases, but you might be surprised to know that CV’s can be just as flawed and are in much broader use as a first parse for algorithms.

How does AI solve the issues of discrimination and bias in recruitment?

At Sapia, we rely on a simple open, transparent interview via a text conversation to evaluate someone for a role. No visuals, no CV data. No voice data as that too carries the risk of bias. Neither do we take data from Facebook. Using nothing that the candidate does not know about.

Bottom line, testing for bias and removing it from algorithms is possible. Whereas for humans, it’s not.

If you want to learn more about how we test for bias and why bias testing is critical to an AI screening tool get in touch here.

No amount of bias training will make you less biased.  Maybe that’s one reason why using machines to augment and challenge decisions is fast becoming mainstream.

It certainly helps to reduce the impact of unconscious bias in hiring decisions.



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Jeff Uden, Head of Talent, Iceland Foods & Sapia

On 26th August, our CEO Barb Hyman facilitated a webinar on “Hiring with Heart” in collaboration with The Recruitment Events Network.

To our surprise, Jeff Uden who is the Head of Talent and L&D for Iceland Foods also joined the webinar.

During the session, Jeff offered some wonderful comments. We took a transcript of Jeff’s input and have jotted it here. It offers insights on dealing with enormous volumes of candidates, offering positive candidate experience and communicating culture from a candidate’s first experience with a brand.

Thanks for your insights, Jeff. Incredibly valuable.

 In conversation with Jeff Uden Head of Talent, Iceland Group & Sapia

At Iceland Foods, we have started working with Sapia. That was as a result of a couple of things. One was the element of the mass recruitment that we were doing. Just to put it in perspective, in the first four months of this year, we received over five hundred thousand applications.

We wanted to find a way that delivered a level of fairness, a level of consistency around how we sift those applications that then enabled store managers to reduce that amount of time that they are spending on doing the recruitment.

The other thing that we wanted to do was significantly enhance our candidate experience. One of the challenges that I had around the experiences that we had within the business is that it felt like it was really standard. It felt like it was cold; it felt like it came from a computer. We wanted to change how we did that and more importantly give something back to the candidates.

Often nowadays people apply for jobs, and there’s the standard ‘bulk’ response that says if you haven’t heard anything from us in two weeks take it that you haven’t been successful.

As big companies or companies of any size we have a duty to help those individuals to understand why they haven’t been successful and to help them to be successful in the next role for which they apply.

The fact that they won’t be hired into your business is probably the right decision because they wouldn’t have been the right fit given the testing that they have gone through. However, that doesn’t mean they are a bad individual. What we need to do is to help them to understand where their strengths are and where their development needs are, and certainly, that was a massive appeal of working with Sapia.

Going through and reading some of the feedback that we’ve had from the candidates, it’s having a huge effect on the candidate experience.

I just had a very quick look at our figures with Sapia, we’ve got a 100% positive experience from our candidates. Bearing in mind 49,000 candidates have gone through Sapia. To have that figure is a superb figure.

We had a swift implementation planned. But probably one of the lengthiest parts of it was about actually getting the questions right and getting the language right. We really did spend a decent period doing that.

I just had a quick look at one of the pieces of feedback here, and this is completely unedited:

“I enjoyed the interview. It makes me believe Iceland as a company are people carers and their staff are more than just employees”.

That’s what’s coming over from the way in which we put the language across within the questions.

We are genuinely really chuffed about how they are engaging far more with us as a brand and how they are feeling like they are getting something back. They genuinely don’t feel like this is a computer process in any way whatsoever; they genuinely feel like they are talking to people. 

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Have you seen the 2020 Candidate Experience Playbook?

If there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.

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