We are often asked by talent leaders and hiring managers whether interviews conducted via a text-based chat disadvantage people who have English as a Second Language (EASL).
While that may seem intuitive, the data tells a different story.
Aggregate results across a variety of Sapia.ai clients that use our AI Smart Interviewer indicate that EASL candidates, in general, perform better than Native English speakers.
While these results may seem surprising, the science that underpins our AI Smart Interviewer has been created to mitigate bias, and we test this constantly.
Standard testing includes the “4/5th rule”, the industry standard test for adverse impact. It ensures the selection ratio of a minority group is at least four-fifths (80%) of the selection ratio of the majority group.
When comparing Native English Speakers with Non-Native English Speakers (EASL), it is shown that EASL candidates are scored higher on average by our AI Smart Interviewer and therefore auto-progressed at a higher rate than those whose native language is English, achieving a 4/5ths rule score of 100%.
Assessing language using Sapia.ai
When it comes to assessing language skills using Sapia.ai proprietary written language assessments, we have developed two aggregate measures called “basic communication skills” and “advanced communication skills”.
– Basic skills look for language fundamentals like spelling, grammar, readability etc.
– Advanced skills look at the sophistication of language (e.g. vocabulary).
It is important to note that the dimensions used within each measure such as spelling and grammar are weighted in such a way that not all misspelled words or grammatically incorrect sentences result in a penalty. These aggregate measures are benchmarked and validated using our large interview dataset across multiple role families.
Further, in Sapia.ai assessments, these measures are not always weighted the same and are set depending on how important language skills are for a particular job.
For example, for a customer-facing retail role, “basic skills” might be set as “medium” and “advanced skills” as “low” or as simply ignored. A retail team member may be required to jot down notes or write the occasional report or email. Basic writing skills may be helpful but not essential, hence the “medium” weighting and minimal impact on their overall score. Other personality traits and behavioral competencies may play a stronger role in determining role-fit.
Secondly, the scores are benchmarked within a relevant population. A retail worker’s “basic skills” score is not compared against graduates or call center staff.
Here’s how the scoring might work:
Maria applies for a retail role and gets a basic skills score that puts her in the top 20% of the population, that is, within a population of retail candidates. This percentile is used in the final score calculation. That way no one is disadvantaged, and candidates are only compared within a comparable group. The basic skills score received by Maria that placed her in the top 20% of retail applicants is 54/100.
In comparison, Michael, a graduate applicant, receives a basic skills score of 72 and is in the top 30% of graduate applicants. Michael has scored higher than Maria in his basic skills, but in their respective populations, Maria has done better than Michael.
There are also other factors to consider when thinking about smart chat interviews and their impact on EASL candidates.
In a spoken test or video test, candidates have fewer chances to re-record their answers. In our Chat Interview™, we give candidates unlimited chances to refine their answers, allowing them to edit the text until they are ready to submit. An EASL candidate will have as much opportunity as they want to refine their answers with no pressure.
Candidates can do the test at their own pace, so the time taken to complete the test is not a factor that will impact the scores. An EASL candidate will have enough time to work on the language and get it right.
You may still be wondering how we ensure EASL candidates’ personality traits and behavioral competencies are also accurately assessed.
Our Chat Interview™ uses Natural Language Processing, machine learning, and optimization methods to score structured interview responses, fairly and consistently.
Our scoring leverages data from over 1 billion words written by over 3.5 million diverse candidates across many different role families and regions.
Based on one’s use of language, we derive signals that matter, like personality and behavioral competencies, that are then used in a predictive algorithm based on the ideal candidate profile to generate a score and recommendation.
We don’t use simple keyword matching, and we consider more than just the words used. Phrasing, syntax, structure, and context all matter. Perfect grammar and spelling don’t matter for the majority of constructs.
Taken together, our highly tuned assessment models combined with the validity of structured interviews represent a far more enjoyable and reliable assessment experience for EASL candidates, especially when compared to traditional assessments.
Being data-driven means we can constantly and vigilantly check that EASL candidates are not disadvantaged in how they are assessed.
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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.
Blog
Keeping Interviews Real with Next-Gen AI Detection
It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.
As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.
Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.
What’s New?
Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate.
This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.
The Challenge of AI in Chat-based Interviews
Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role.
For Candidates: Enabling Authenticity
When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences.
For Hiring Teams: Actionable Insights
Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines.
Built on Unmatched AI Interview Expertise
“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.
Why This Matters
The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:
“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”
Testing and Validation of the AGC Detector 2.0
The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.
Fairness & Transparency in AI-Enabled Hiring
By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.