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.
Related
Blog
Mirrored diversity: why retail teams should look like their customers
Walk into any store this festive season and you’ll see it instantly. The lights, the displays, the products are all crafted to draw people in. Retailers spend millions on campaigns to bring customers through the door.
But the real moment of truth isn’t the emotional TV ad, or the shimmering window display. It’s the human standing behind the counter. That person is the brand.
The missing link in retail hiring
Most retailers know this, yet their hiring processes tell a different story. Candidates are often screened by rigid CV reviews or psychometric tests that force them into boxes. Neurodiverse candidates, career changers, and people from different cultural or educational backgrounds are often the ones who fall through the cracks.
And yet, these are the very people who may best understand your customers. If your store colleagues don’t reflect the diversity of the communities you serve, you create distance where there should be connection. You lose loyalty. You lose growth.
We call this gap the diversity mirror.
What mirrored diversity looks like
When retailers achieve mirrored diversity, their teams look like their customers:
- A grocery store team that reflects the cultural mix of its neighbourhood.
- A fashion store with colleagues who understand both style and accessibility.
- A beauty retailer whose teams reflect every skin tone, gender, and background that walks through the door.
Customers buy where they feel seen – making this a commercial imperative.
How to recruit seasonal employees with mirrored diversity
The challenge for HR leaders is that most hiring systems are biased by design. CVs privilege pedigree over potential. Multiple-choice tests reduce people to stereotypes. And rushed festive hiring campaigns only compound the problem.
That’s where Sapia.ai changes the equation: Every candidate is interviewed automatically, fairly, and in their own words.
- Bias is measured and monitored using Sapia.ai’s FAIR™ framework.
- Outcomes are validated at scale: 7+ million candidates, 52 countries, average candidate satisfaction 9.2/10.
- Diversity can be measured: with the Diversity Dashboard, you can track DEI capture rates, candidate engagement, and diversity hiring outcomes across every stage of the funnel.
With the right HR hiring tools, mirrored diversity becomes a data point you can track, prove, and deliver on. It’s no longer just a slogan.
Retail recruiting strategies in action: the David Jones example
David Jones, Australia’s premium department store, put this into practice:
- 40,000 festive applicants screened automatically
- 80% of final hires recommended by Sapia.ai
- Recruiters freed up 4,000 hours in screening time
- Candidate experience rated 9.1/10
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
Recruiting ideas for retail leaders this festive season
As you prepare for festive hiring in the UK and Europe, ask yourself:
- How much will you spend on marketing this Christmas?
- And how much will you invest in ensuring the colleagues who deliver that brand promise reflect the people you want in your stores?
Because when your colleagues mirror your customers, you achieve growth, and by design, you’ll achieve inclusion.
See how Sapia.ai can help you achieve mirrored diversity this festive season. Book a demo with our team here.
FAQs on retail recruitment and mirrored diversity
What is mirrored diversity in retail?
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Why is diversity important in seasonal retail hiring?
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
How can retailers improve their hiring strategies?
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
Blog
The Diversity Dashboard: Proving your DEI strategy is working
Why measuring diversity matters
Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.
Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.
Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.
What is the Diversity Dashboard?
The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.
Key features include:
- Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
- Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
- Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
- Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
- Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.
From insight to action
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
- See if your EASL candidates are applying in high numbers but not progressing to live interview.
- Spot if candidates with a disability report high satisfaction but have lower offer rates.
- Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.
Built on science, backed by trust
Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.
Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.
Book a demo to see the Diversity Dashboard in action.
Blog
Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.