Action and data
COVID has taught us that on reflection the focus on individual action with a community benefit as a goal is really a focus that leads to the greater good. In our home state of Victoria, Australia now 7 straight days with ZERO new cases. It has been an effort founded on facts and science over misinformation. In Victoria, many sacrificed a lot for their well-being for ALL. If anything, there is now proof, thanks to Victorians, that when we see facts, listen to science and let data show you how to lead that change, you can make it happen.
We’re using this approach to build a new vision for inclusive hiring.
AI, especially predictive machine learning models, are an outcome of a scientific process, it’s no different to any other scientific theory, where a hypothesis is being tested using data.
The beauty of the scientific method is that every scientific theory needs to be falsifiable, a condition first brought to light by the philosopher of science Karl Popper. In other words, a theory has to have the capacity to be contradicted with evidence.
It is how science is able to progress without bias.
There are three decisions that are made by a human in building that scientific experiment.
- Forming a meaningful hypothesis
- Data collection methodology (experiment set up)
- The data you rely on to test the hypothesis
One can argue 2 and 3 are the same as if the methodology is not sound the data collection wouldn’t be either. That’s why there is so much challenge and curiosity as there should be about the data that goes into an algorithm.
Think of an analogy in a different field of science: the science of climate change.
A scientist comes up with a hypothesis that certain factors drive an increase in objective measures of climate warming, eg CO2 emissions, cars on the road, etc. That’s a hypothesis and then she tests it using statistical analysis to prove or disprove that her hypothesis holds beyond random chance.
The best way to make sure you are following a sound scientific approach is to share your findings with the broader scientific community. In other words, publish in peer-reviewed mediums such as journals or conferences so that you are open to scrutiny and arguments against your findings.
Or to put it another way, be open for your hypothesis to be falsified.
In AI especially, it is also important to keep testing whether your hypothesis holds over time as new data may show patterns that lead to disproving your initial hypothesis. This can be due to limitations in your initial dataset or assumptions made that are no longer valid. For example, assuming the only information in a resume related to gender are name and explicit mention of gender or a certain predictive pattern such as detecting facial expressions are consistent across race or gender groups. Both of these have been proven wrong*.
The only way to improve our ability to predict, be it climate change or employee performance, is to start applying the scientific method and be open to adjusting your models to better explain new evidence.
Therefore the idea that a human can encode their own biases in the AI — well it’s just not true if the right science is followed.
* Amazon scraps secret AI recruiting tool that showed bias against women (https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G)
* Researchers find evidence of bias in facial expression data sets (https://venturebeat.com/2020/07/24/researchers-find-evidence-of-bias-in-facial-expression-data-sets/)
To keep up to date on all things “Hiring with Ai” subscribe to our blog!
You can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo
Related
Blog
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:
-
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.
-
Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
-
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.