The marriage of behavioural science, data science and AI technology
The introduction of artificial intelligence (AI) technologies into the world of HR and recruitment is not just an idea anymore, it is a reality, specifically focusing on AI for HR. Neural networks, machine learning, and natural language processing are all being introduced into different areas of HR, marking a significant shift towards integrating AI for HR purposes.
These developments contribute to the function’s increased accessibility to data-driven insights and analytics, enabling better-informed people decisions.
In recruitment and talent acquisition, AI technologies have the potential to make a significant impact by identifying candidates who can perform well in individual business environments.
However, pre-hire assessment is a complex area, and without incorporating validated behavioural science we only end up with a 2D view – instead of the 3D view we actually wanted. This is why the marriage of data, computer and behavioural sciences is essential.
By bringing together organisational psychologists, data scientists and computer scientists we truly leverage the power of artificial intelligence – and change the way candidates are recruited. It takes the recruitment process beyond the technical excellence necessary to collect and report on data and insights.
By merging these scientific areas we get:
- Computer science expertise providing the critical ‘how’ for collecting quality data.
- Data science brilliance then revealing the ‘what’ of unseen connections within that data.
- Well-constructed behavioural science explaining the ‘why’ behind those connections.
Through the combination of all three disciplines, we can access a whole extra world of meaning, enabling us to get closer to the core of what’s happening in organisations.
Behavioural science is the key to success
A recent Industrial & Organisational Psychology article pointed to the disruption taking place in the talent identification industry through new digital technologies. The authors noted that although big data is attractive, the data is often thrown together and interrogated using data science until correlations are found. This has become known as ‘dustbowl empiricism’.
My favourite for this at the moment has to be the strong correlation between the number of people who have drowned by falling in a pool, and the number of films Nicolas Cage has appeared in any given year. Who knew how dangerous Nicolas Cage could really be?
Despite the evident danger of watching Nicolas Cage films (particularly near water), I believe there is more value in explaining behaviour than in just predicting it.
For example, is there a correlation between owning a certain type of car and being a high performer?
Perhaps, but I don’t think to look for the best candidates in car parks is very useful. After all, people change cars, and so might the correlations change between particular car models and performance. To cite another famous example, as often as people change their eating preferences, so goes the link between curly fries and intelligence.
Understanding why data is linked can suggest better ways to improve performance than just updating the carpool or changing the canteen menu.
Linking a vehicle preference to well-established behavioural science may suggest that a client considers how a candidate is innovative elsewhere in their lives, such as in their adoption of other new technologies. Or they may look for other ways the candidate demonstrates a penchant for reliability (perhaps through previous work choices).
The scientific approach
This is where organisational psychologists come in.
They have an intimate knowledge of the theories that can help interpret and explain the links between personal attributes and performance, or other variables that matter. They know how to use these theories to solve real problems and they know how to design studies and measurement tools to ensure that scientific knowledge is applied correctly in an organisational setting.
I learned a lot of organisational psychology models and theories during my Masters and PhD studies. We focused on these and the research behind them when I taught MBA and Master of Organisational Psychology programs – sometimes noting gaps in current models and theories – and designing studies to help extend or debunk what we knew.
While completing my MBA and later in a corporate role, I became skilled in applying that knowledge to the problems managers and executives face.
As an organisational psychologist I often find that it isn’t just knowing behavioural science that matters, it is knowing the behavioural science detail to understand what is most relevant for a role or business problem.
For example, consider sales performance.
Thanks to the popularity of some psychometric instruments, ‘extroverted’ or ‘introverted’ are understood as reliable ways to describe elements of a person’s personality, and many people are convinced that being extroverted is important in a sales role.
However, the research on sales performance says otherwise. An International Journal of Selection and Assessment article shows that across a range of studies there isn’t a strong link between ‘extraversion’ (broadly) and sales performance, despite this being such a common view.
Knowing the detail matters here.
A broad description of extraversion may not do a candidate justice, particularly when we’re focused on understanding performance in a particular role.
Instead, we might be interested in a candidate’s level of dominance, their sociability, what they would be like in a group setting, or presenting to a group to make a sale.
Perhaps we’d be interested in whether they are independent, adventurous, or ambitious, all of which (as potential elements of extroversion) may have different implications for sales performance.
We might also focus on the particular nature of the sales role – many roles are becoming more formalised and structured, with down-to-the-minute journey plans and call times. No wonder then that the Journal of Selection and Assessment article found another personality factor, conscientiousness, to be relevant for predicting sales performance.
The business focus of pre-employment assessments
It’s the acceptance of how important behavioural science is to the new world of AI that has led me to Sapia, where we believe all people decisions should be based on science, data and analytics – not just gut feeling.
Sapia focuses on the things that matter.
We use validated behavioural science to build predictive models, centred on the issues your business wishes to address and their corresponding KPIs. The predictive model is based on your workforce data so it’s unique to your organisation, maximising predictive accuracy while also prioritising the candidate experience.
We use various techniques, including training a neural network to identify what drives performance in the organisation, based on the data we collect. We build our algorithms to achieve accurate predictions from the start, and the model improves over time through machine learning.
We’re now at a point where we can use behavioural science, data science and computer technology to understand the intricate links between candidate information and performance data. With that we can help reduce bias and level the candidate playing field and give managers a 3D view of their candidates, to enable them to make the best people decisions.
Dr. Elliot Wood is a registered organisational psychologist with a bachelor’s degree, various master’s degrees and a PhD in the field. He spent 12 years in academia, teaching master’s-level organisational psychology; supervising post-graduate research; and working on research grants and consulting projects. He then moved into organisational development–focused consulting in Australia and Asia, followed by an internal talent role in a multinational brewer. He is now Chief Organisational Psychologist at Sapia.
References
Tomas Chamorro-Premuzic, Dave Winsborough, Ryne Sherman and Robert Hogan, Industrial & Organisational Psychology, ‘New Talent Signals: Shiny New Objects or a Brave New World?’
Murray R. Barrick, Michael K. Mount, Timothy A. Judge, International Journal of Selection and Assessment, ‘Personality and Performance at the Beginning of the New Millennium: What Do We Know and Where Do We Go Next?’
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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: