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?
- 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.
- 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
- 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. - 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.
- 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.
____________________
[1] https://podcastnotes.org/2019/10/18/daniel-kahneman-decision-making/
Related
Blog
Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: