Depending on which media you read, technology, and specifically Artificial Intelligence, will create or destroy thousands of jobs. It is already radically changing many, as well as how we apply and hire for them.
Fear of New Invention
Back in the day when cars were first released, there was such a fear about the danger they presented to society, that when they came to a junction, they were required to stop the car, get out and fire a warning shot so that the people in the surrounding area would be safe from unexpected danger.
I was reminded of this when reading the commentary around Amazon and its use of AI to screen talent.
Amazon’s AI Experiment Went Awry
In case you missed it, Amazon did an experiment. They analysed 10 years of CV data to build a predictive model to help filter through what I am sure is hundreds of thousands of applications to work at the company. Because the sample group was mostly male, the CVs were naturally based towards male ‘traits’ if there is such a thing. The model built off this training data naturally ended up mirroring that sample group which meant it preferred male to female CVs.
CV’s Trigger Bias
It is pretty obvious to all of us that if you create a product off one homogenous group, then you will end up flavouring it with the characteristics of that group. YouTube found that when the team they used to build their iOS app didn’t consider left-handed users when it added in mobile uploads, causing videos recorded in a left-handed person’s view of the landscape to be upside down. I presume because the team building it was comprised of all right-handed people.
Suggested reading: A CV Tells You Nothing
Humans are heavily prone to unconscious bias
In fact, we rely on biases to survive.
While these biases help us not go insane, unfortunately, it has led us to the point today where they are having a very significant effect in the workforce. There are many serious forms of bias, but the best known is gender bias. A recent study showed simply by changing the name of an applicant from a woman’s to a man’s, with every other detail kept the same, the ‘male’ applicant was more likely to progress to an interview. The exact same CV.
When humans do screening, they are prone to making snap judgements based on superficialities, ignoring the very many factors that can help actually predict whether a candidate will perform. This is where data platforms actually have an advantage, by doing ‘blind screening’ and making the process both faster and fairer. However, this only works when the data that goes into the model manages for human frailties.
When it comes to using data to build predictive models to inform and guide decision-making, it is important to really dig deep on the input data.
And if you think unconscious bias training is the answer … read this first.
Think carefully about input data
The key insight for this experiment for Amazon is that relying on CVs to assess talent, is inherently flawed. This is accentuated even more when you accept that what differentiates talent now and will become even more acute in the future is not hard skills, not what uni someone went to or degree they have, but soft skills. Jeff Weiner who has the benefit of this kind of rich data from 600m users attested to that this week.
https://qz.com/work/1423267/linkedin-ceo-jeff-weiner-the-main-us-skills-gap-is-not-coding/
At Sapia, working with dozens of companies across the world to help blind screen thousands of candidates, we know that it’s the behaviours and values of a potential coworker that will influence their performance and tenure. Values, such as commitment and attitudes are invisible in a CV. It’s not easy to see either in an interview. But it’s easily tested using well-crafted data platforms.
So let’s try to look beyond the news grab, the headline which naturally attracts attention when it has Amazon in the first line.
- Algorithms will be biased if the data they are built with is biased.
- Algorithms can be tested for bias. Humans can’t be
- Algorithms can be trained to remove bias. Humans, truthfully cant be
- Algorithms are blind to your gender, skin colour age. Humans are very sensitive to this, especially when it comes to hiring
We have a once-in-a-Millenium opportunity to extend and enable better, fairer thinking through careful and conscious AI-assisted decisions.
The algorithms we build aren’t sentient beings or unmanageable acts of nature, they are built by humans. When we recognise that and are conscious of those risks, we can start to counteract these biases through technology to help humans see what’s in front of us more clearly, without the filters of bias.
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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.
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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.