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.
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We cover this and so much more in our report: Hiring for Equality.
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[1] https://podcastnotes.org/2019/10/18/daniel-kahneman-decision-making/
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Joe & the Juice Partners with Sapia.ai, Scaling an Exceptional Candidate Experience and Cutting Time to Hire
Read the full press release about the partnership here.
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.
But, as their footprint expands, so does the challenge of finding and hiring the right talent to embody their unique culture. With over 300,000 applications annually, the traditional hiring process using CVs was falling short – leaving candidates waiting and creating inefficiencies for the recruitment team. To address this, Joe & The Juice turned to Sapia.ai, a pioneer in ethical AI hiring solutions.
A Fresh Approach to Hiring
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.
Candidates receive an engaging and fair interview experience as well as personality insights and coaching tips as part of their journey. In fact, 93% of candidates have found these insights useful, helping to deliver a world-class experience to candidates who are also potential guests of the brand.
“Every candidate interaction reflects our brand,” Sebastian Jeppesen, Global Head of Recruitment, shared. “Sapia.ai makes our recruitment process fair, enriching, and culture-driven.”
Results That Matter
For Joe & The Juice, the collaboration has yielded impressive results:
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33% Reduction in Screening Time: Pre-vetted shortlists from Sapia.ai’s platform ensure that recruiters can focus on top candidates, getting them behind the bar faster.
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Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
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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.”
Trailblazing for the hospitality industry
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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Sapia.ai Wrapped 2024
It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
Blog
Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
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.