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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Joe & the Juice, the trailblazing global juice bar and coffee concept, is renowned for its vibrant culture and commitment to cultivating talent. With humble roots from one store in Copenhagen, now with a presence in 17 markets, Joe & The Juice has built a culture that fosters growth and celebrates individuality.
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Through this partnership, Joe & The Juice has transformed its hiring process into an inclusive, efficient, and brand-aligned experience. Instead of faceless CVs, candidates now engage in an innovative chat-based interview that reflects the brand’s energy and ethos. Available in multiple languages, the AI-driven interview screens for alignment with the “Juicer DNA” and the brand’s core values, ensuring that every candidate feels seen and valued.
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Bias-Free Hiring: By eliminating CVs and integrating blind AI that prioritizes fairness, Joe & The Juice ensures their hiring reflects the diverse communities they serve.
Frederik Rosenstand, Group Director of People & Development at Joe & The Juice, highlighted the transformative impact: “Our juicers are our future leaders, so using ethical AI to find the people who belong at Joe is critical to our long-term success. And now we do that with a fair, unbiased experience that aligns directly with our brand.”
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In an industry so wholly centred on people, Joe & the Juice is paving the way for similar brands to adopt technology that enables inclusive, human-first experiences that can reflect a brand’s core values.
If you’re curious about how Sapia.ai can transform your hiring process, check out our full case study on Joe & The Juice here.
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Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.