There has been some negative media attention lately surrounding the use of Artificial Intelligence (AI) in the recruitment space with warnings ranging from the fact that AI produces a shallow candidate pool to more serious things like amplification of bias.
There are many instances of AI being used in a way that has harmful outcomes, but it is important to clarify that this is about how AI is being implemented and not an issue with the use of AI itself.
When AI is used appropriately, responsibly, and following regulatory guidelines it is an incredibly powerful tool that can create fair outcomes for candidates who are selected without bias – in a way that no other tool at our disposal can.
This is why we think it’s worthwhile that more people better understand AI and some of the differences in the way it is used and implemented.
There is no unified Ai
Most media articles refer to AI as if it represents a singular master algorithm and fail to identify how varied the implementations of it are. Almost all AI we have today falls into the category of “narrow AI”, in other words algorithms, mostly machine learning, built to solve a specific problem. E.g. classify sentiment, detect spam, label images, parse resumes. These purpose built AI are highly dependent on the nature of the underlying training data and the expertise of the developers in making the right assumptions and tests of validity of their models. When built in the right way and used responsibly, AI has the ability to empower humans. This is why at Sapia.ai we have made various conscious design choices and adhered to a framework called FAIR™ that tests for bias, validity, explainability and inclusivity of our AI based tools.
Text over video
The biggest cause for alarm is when AI is applied to analysing video, which can lead to irrelevant inputs like clothing, background, and lighting being used as predictors of personality and job-fit. Video and speech patterns also make it nearly impossible to remove demographic information like race and gender as inputs.
Additionally, analyzing facial expressions is problematic, especially when evaluating certain candidates like those with Autism Spectrum Disorder or other forms of neurodiversity.
This is why Sapia.ai does not, and will not, use AI scoring for video interviews or even voice transcriptions from videos or audio given the word error rate introduced in transcribing speech. Instead, we opt for text – which we implement in a friendly no pressure environment that feels like you are texting a friend.
It’s worth noting that no data other than the answers given by the candidate are used in the ‘fit score’ calculation – that is, we never use demographic data, social media, CV or resume data (which also contain demographic signals, even when de-identified), or behavioral metrics such as time to complete.
Even a candidate’s raw text itself contains gender and ethnicity signals that can introduce bias, if not mitigated. This is why we only use feature scores (e.g., personality, behavioral competencies, and communication skills) derived according to a clearly defined rubric in our scoring algorithms, which our extensive research shows contain significantly less gender and ethnicity information than raw text.
Aim to uncover hidden talent while measuring potential
Another common concern is that AI will result in more uniformity rather than diversity in the workforce as algorithms narrow the pool in order to search out an employer’s ideal candidate. There are several things worth noting here.
First, identifying what the ideal candidate is – that is, what knowledge, skills, abilities, and other characteristics are important for success in the role – is what a job analysis is for and should, legally, be what your selection tool is designed to measure.
This is also not specific to AI, as all selection systems are designed to identify which candidates have a profile of traits and characteristics that indicate they will likely be successful in the role. This doesn’t automatically mean that every hire is going to be exactly the same, though. When you focus on the traits and characteristics that will set someone up to be successful, considering potential more than background or pedigree, you’re more likely to uncover hidden talent and hire more successful people from a broader, more diverse range.
Relying solely on past data to build your model also runs the risk of introducing historical data biases. This is actually why it is so important to consider the ideal candidate profile and use that to inform your scoring model. We strongly believe in keeping the human in the loop, which is why our scoring models are centred around the human-determined (via job analysis) ideal candidate profile and then optimized to ensure all bias constraints (e.g., 4/5ths rule and effect sizes) are met.
Using this approach, Sapia has helped clients achieve their DEI goals and increase their diversity hires, including impressive statistics like hiring 3x more ethnic minorities, 1.5x more women, and 2x more LGBTQ+ candidates in just 3 months.
Keep Ai processes transparent
Lastly, it’s worth acknowledging that there is often a “black box” mystery of how AI recruitment tools work. People don’t trust what they don’t understand. While we don’t expect everyone to be an expert in AI or Natural Language Processing, we do strongly believe in building trust through transparency and work hard to make sure that our models are easily understood and open to scrutiny. From third-party audits to detailed model cards to in-depth dashboarding and reporting, we aim to maximize transparency, explainability, and fairness.
We believe a fairer future can only be achieved when AI is used responsibly. AI is not the enemy, rather it’s the experience and motivation behind those promoting it that can make the difference between what is good AI and what is harmful AI.
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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.
Blog
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.