Written by Nathan Hewitt

Myth or fact: Marginalised people are more likely to drop out when Ai tools are involved?

We get asked this a lot. It’s an important question, especially when it comes to creating a fair playing field among candidates looking for jobs.

There are two things that we do differently with our Ai technology that means our Ai is an improved experience for marginalised candidates. 

Firstly, we use chat. Chat allows you to write in your own time, use your own words, and be happy with what you submit, when you are ready to submit it. It does not judge you visually, nor do our algorithms score you badly for typos, or having English as a second language. 

Secondly, we use clean input data. Data from CVs carry inherent bias around gender, socio-economic standing, ethnicity, and age. It does not matter if the process is ‘blind’ (i.e. there is no name or gender attached to the CV), over time the machine will start to favour those that society has (largely white men). By using only data that is given in the interview through chat, our data is objective.

Chat doesn’t feel like an assessment, and it allows you to be interviewed in a familiar way. Think how different this is to platforms that gamify the recruitment process, creating a stressful and uncomfortable experience for many people. Chat creates a safe space to be yourself.

Recently, we undertook a piece of research for a large national retailer with regard to improving their experience for Aboriginal and Torres Strait Islander peoples, the Indigenous peoples of Australia. We took 2,454 applicants who self-identified as Indigenous Australians and compared this to a group that didn’t identify as such.

The analysis revealed that the retail group hired 1.2x people who identified as Indigenous Australians in their candidate pool. Candidate feedback rating was 9.37/10 (3% higher than non-Indigenous) highlighting the appeal of the platform.

Indigenous Australians have been overlooked for so long when it comes to jobs. When we gave them a fair chance and an experience that let them tell their story, something so inherent to their culture, they were elevated as candidates. 

This is truly a profound outcome, and one we believe can change the lives of so many people traditionally overlooked for roles. 


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.


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Could ‘Personalized Work’ Be What We Aim For Post-Covid?

Last week, the jewel of Australia’s tech sector, Atlassian, was lauded for giving staff the privilege of working from home – forever.

After posting this on our team slack channel with a comment by me warning of the longer-term impact of ‘remote forever’, one of our senior team members said:

“Why do people travel in the morning to an office? In a packed tram/train carrying a laptop , then work on that laptop only to carry it back home in a packed train, wasting precious time?”

When I worked for another technology company, we spent a lot of energy trying to convince leadership that WFH did not mean a free ride. And, in fact, would unleash productivity and improve engagement. COVID has brought forward the idea of WFH as an alternative arrangement for many that wouldn’t have otherwise considered it.

Whilst we may be revelling in the success of dismantling the long-held bias, that you need to see someone at work to trust that they are doing the work, it comes with its own set of challenges around organisational relevance.

Work is a relationship

Does it matter what company you work for if the only difference between one job is for whom you are completing a task, and perhaps the one or two people that you work with closely?

Work is a relationship, and relationships thrive on intimate and frequent connections. When we all worked in offices some of that intimacy was built by the serendipity of conversations that you had while going about your day’s work. There was always the potential to catch someone from outside of your team and share an idea and solicit a different perspective.

There was an ease of connections and interactions that can be hard to replicate in a remote work context.  Being remote is a little bit like trying to establish a long-distance relationship. Which all of us know have the chances of success stacked against them.

Then there is the influence of place, and of space. At REA Group where I worked for some years the building fed the culture. Its design and redesign were carefully thought through to maximize connections and space to collaborate. With anyone. Not just those in your immediate team.


Why do people go to church to pray, the pub to drink, and the footy to watch their team, when they have the bible at home, beer in the fridge, and a TV in the living room? Because they are looking for connection, community, and inspiration.

Once the novelty of WFH wears off, and for many it already has, comes the very real challenge of maintaining connection, building affiliation, and building cultures when people and teams are not spending time together – physically, in any shared space.

Ongoing remote work presents very practical challenges for organisations, particularly around company culture and organisational HR.

  • How do you assess performance when you can’t see people at work?
  • And how do you look out for people, mentor them, develop them when your interactions are all booked in, bound within a strict working day?
  • How do you acknowledge someone for something you heard they did well in another meeting like you might as you jump in a lift together?

There is a real risk that our employment relationship becomes transactional, which then impacts engagement, which then impacts productivity etc.
We know from our own work in this space, personality is not 16 types on a table, it is way more nuanced and diverse than that. In a population of 85,000 equal men and women, we find at least 400 uniquely identifiable personality types.


While we live in a world of hyper-personalization – our morning news feed is our feed, our Netflix profile is our personal profile based on our viewing history,

How can an organisation retain that diversity of perspective when it usually thinks of two binary ways of working – in an office, or at home? It can’t.

That’s why the future of work has to involve a new type of technology, a technology that can navigate the rich mix of types we work with, adapt to their communication style, their working style.

While I have championed for WFH in senior HR positions I’ve held, this experience has highlighted for me the many things I might have always taken for granted in an office environment.

It has nothing to do with fancy décor and an ergonomic chair. More those human moments of serendipitous connection. It all disappeared so quickly without almost any time to say good-bye.

I’m learning what my motivations are, and what connections I want in a day.

From the conversations I’ve had with friends and workmates, they’re also making similar self-discoveries. I’d like to think we all emerge from this situation with a mind to honour the things we’ve learned about our “work selves.”

And most importantly, to build company cultures that thrive by accommodating those diverse needs.

Barbara Hyman, 03/09/20


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Recruiters are stuck with busywork. How can tech free up their time?


Kyle Lagunas:
You and I both know that adding more headcount will not help the issue [of recruiters being overworked], since it’ll just result in more people doing more tasks.

At one point, we had General Motors in a position where we were having quarterly go-to-market meetings every quarter.

As a leadership team, we met to determine what we wanted to achieve in the next quarter and what it would take to get there.

When I started running the go-to-market functions for my boss, Cyril George, I told him that no one here knew what their KPIs were because it wasn’t clear; it was like everything was on fire all the time.

So we began having these go-to-market meetings, and a significant portion of them focused on the tech and innovation that we were driving to resource the team.

Then someone asked, “What’s the point once we implement all of this?”

I knew the subtext was, “Are we laying people off? Are we getting rid of recruiters?”

I responded, “No, the point is for you not to be working 65 hours a week every week.”

The room fell silent; there was no slow clap, just disbelief and shock.

They thought, “I don’t think that’s real,” but it is.

That’s what tech can do, you know.

Not only can it help for one quarter, but it can also make a difference for years to come.

So, stop thinking of buying tech for new best practices that it can bring, and start thinking of it as a way to extend our capacity sustainably and meaningfully.

It’s critical.

Barb Hyman:
Yeah, I see it the same way, in terms of giving you leverage.

Every time you hire someone for your team, you gain more leverage, allowing you to achieve more.

Technology does the same thing, but on a larger scale.

Listen to the full episode of our podcast featuring Kyle Lagunas here:

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