To find out how to interpret bias in recruitment, we also have a great eBook on inclusive hiring.
Blind hiring and screening approaches have become significant in recruitment recently and are now considered fair and objective. But what is blind screening? The blind screening involves a situation where the candidate’s personal information such as name, gender, age, or ethnicity is not known to the employer. This is to avoid the bias of conscience and make the process of employment be fair or meritocracy.
One of the AI-enhanced interviewing practices is the AI blind interviews, where an AI interviewer does not know about the personal and demographic details of the candidate. At times, AI blind interviews may also employ voice modulation to guarantee total anonymity. On the other hand, blind resumes are carefully edited versions, where personal information is removed, leaving only skills and experience, often processed through AI interview software.
Blind recruitment is defined as the process of making hiring decisions without regard to personal and demographic details. This approach has gained momentum, and recent blind hiring data indicate that organizations that practice these methods tend to see a rise in diversity as well as a decrease in hiring bias.
In the late 1970s, as the world was changing around them, the Toronto Symphony Orchestra realised they had a problem. Specifically, a white male problem; the profile of nearly every musician.
In what is largely seen as the genesis of the blind interview, in 1980 the orchestra changed their audition process completely. Musicians were placed behind a screen so the auditioning panel couldn’t know the gender, race or age of the musician they were listening to. It’s said they even put down the carpet so the sound of high heels on the stage could not be heard.
All the panel could hear was the music.
Of course, the result of this blind screening was profound. Hiring decisions were made on the quality of the performance only. In just a few short years, the ‘white male’ orchestra was transformed to more equal gender representation with musicians further diversified by their cultural backgrounds.
Not only has the Toronto Symphony Orchestra continued to use blind screening ever since, but it was also quickly adopted by most major orchestras around the world.
Beyond the concert stage, blind screening and blind recruitment practices are used by government, academic and business organisations globally. Because when it comes to identifying the best qualified or best-fit candidates, all you need to hear is their ‘music’.
Are tall people more likely to get higher paid roles? Do the best looking candidates always get the job? Will Michael or Mohamed be the best fit for your team?
While it’s easy to recognise bias in other people, it’s usually harder to admit that we are biased ourselves. That’s why it’s called unconscious bias. It’s something we all have and something we can all be affected by.
Unconscious bias is about making assumptions, stereotyping or a fear of the unknown in how we assess other people. It can be innate or it can be learned and it’s created and reinforced through our personal experiences, our cultural background and environment.
Think of gender bias, ageism, racism or name bias – these are some common biases that need no explanation. However, psychologists and researchers have identified over 150 types of bias that impact the way we form opinions and make judgements about people, often instantly.
In a two year study titled Whitened Résumés: Race and Self-Presentation in the Labor Market published in the Administrative Science Quarterly in 2016, academics from the University of Toronto and Stanford University looked at racial and gender bias during resume screening.
In one US study, they created and sent out resumes for black and Asian candidates for 1,600 advertised entry-level jobs. While some of the resumes included information such as names, colleges, towns and cities that clearly pointed out the applicants’ race or status, others were ‘whitened’, or scrubbed of racial clues.
Amongst many insights, they found that white-sounding names were 75% more likely to get an interview request than identical resumes with Asian names and 50% more likely than black-sounding names. Males were 40% more likely to get an interview request than women.
Still need convincing?
Another 2016 study by The Institute for the Study of Labor (IZA) in Bonn, Germany examined how ethnicity and religion influenced a candidate’s chances of landing an interview. 1500 real employers received otherwise identical applications, complete with a photo, from Sandra Bauer, Meryem Ӧztürk, or Meryem Ӧztürk wearing a headscarf.
These are just two of many research studies that suggest bias and discrimination are rife in the hiring process. In a 2017 UK study, only a third of hiring managers felt confident they were not biased or prejudiced when hiring new staff, while nearly half of those surveyed admitted that bias did affect their hiring choice. 20% couldn’t be sure.
When it comes to hiring, we all have our own thoughts about what an ideal candidate is supposed to look like. The problem is that our own bias can get in the way of the right decision.
If you’ve already pre-determined a candidate’s suitability by their age, gender or the school they attended, then you could be missing out on employing the candidate with the best qualifications. Or while you’re thinking about the best ‘cultural fit’ for your team, you’re actually missing the opportunity for the best ‘cultural add’.
But what if you could take bias out of candidate screening and hiring process? Is that even possible?
Just as the Toronto Symphony Orchestra hid the identities of auditioning musicians behind a screen, there are several ways to bring blind hiring to your recruitment process:
Nearly all hiring decisions will involve a human to human interview. But take a step back in the process and blind screenings can ensure that all candidates are competing on a level playing field. With the opportunity to be assessed only on qualifications or skills, the best candidates for a role can be identified.
Blind screening is about making candidates anonymous – removing details from applications or CVs that reveal details that may colour the recruiter or hirer’s assessment. It makes it easier to make objective decisions about a candidate based on skills, experience and suitability without the distraction (and the damage!) of bias.
Unconscious bias can be triggered by someone’s name, their gender, race or age, the town they grew up in or the schools they attended.
Before making a final decision, many employers like to test a candidate’s skills or knowledge by setting a task or challenge. Others undertake personality or other testing to assess a range of relevant qualities such as aptitude, teamwork, communication skills or critical thinking. Candidates can be assigned an identifying number or code to retain their anonymity through blind testing, though this is often best done through a third-party service provider.
With face-to-face, phone or video interviews, it’s clearly impossible to keep candidates anonymous. Blind interviewing is possible, however, using a written QandA format or by using next-generation chatbots or text-driven interview software. Most recruiters and employers would agree, however, that there would be few if any, times it would be appropriate to make hiring decisions based solely on blind interviewing and without an in-person interview.
Read: The Ultimate Guide to Interview Automation
Sapia is a leading innovator and advocate in using technology to enhance the recruitment process. Our AI-enabled, text chat interview platform has been designed to deliver the ultimate in blind testing at the most important stage of the recruitment process: candidate screening.
Firstly, you will never have to read another CV again. Especially in bulk recruiting assignments, Sapia can help recruiters find the best candidates faster and more cost-effectively. CV’s are littered with bias-inducing aggravators. With Sapia, blind interviews are at the top of the recruiting funnel, not CV reviews.
By removing bias from the screening process, we’re helping employers to increase workplace diversity. It also delivers an outstanding candidate experience.
Reviewing and screening CVs is the most time-consuming part of any recruiter’s job and Sapia can put more hours back in your day.
Sapia evaluates candidates with a simple open, transparent interview via a text conversation. Candidates know mobile text and trust text.
Our platform removes all the elements that can bring unconscious bias into play – no CVs, video hook-ups, voice data or visual content. Nor do we extract data from social channels.
What candidates do discover is a non-threatening text interview that respects and recognises them for the individual they are, providing them with the space and time to tell their story in their words.
As candidates complete and submit their interview, the platform uses artificial intelligence and machine learning to test, assess and rank candidates on values, traits, personality, communications skills and more. By bringing this blind interview into the upfront screening, recruiters can gain valuable personality insights and the confidence of a shortlist with the very best matched candidates to proceed to live interviews.
The platform has a 99% satisfaction rate from candidates and they report they are motivated by the personalised feedback, insights and coaching tips that the platform provides, along with the opportunity to provide their feedback on the process.
Free from biases of the candidate’s race, gender, age or education level, Sapia’s platform delivers blind interviewing, testing and screening in one. Helping to build workplace diversity brings benefits for everyone – it can help lift employee satisfaction, boost engagement and productivity and enhance the reputation of your business as a great employer.
We believe there is a formula for trust when it comes to interviewing …
Final human decision supported by objective data. Or more simply:
Trust = (Inclusivity + Transparency + Explainability + Consistency) – Bias
Find out more about our AI-powered blind recruitment tool and how we can support your hiring needs today. You can try out Sapia’s Chat Interview right now – here. Else you can leave us your details to receive a personalised demo
It offers a pathway to fairer hiring. Get diversity and inclusion right whilst hiring on time and on budget.
In this Inclusivity e-Book, you’ll learn:
A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.
Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.
We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.
While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.
For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.
This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.
Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.
Enter conversational AI.
Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach.
Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.
The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.
Importantly, every candidate receives the same questions, evaluated against the same objective, explainable framework. These interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.
The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.
One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.
The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.
And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.
Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.
Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.
Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.
This signals respect from the employer. It says: We trust you to show us who you are.
Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.
It’s time for a new way to map progress in AI adoption, and pilots are not it.
Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.
And here’s what struck me:
Despite the hype, we still don’t have a shared language for AI maturity in business.
Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.
The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.
It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.
Here’s an early sketch based on what I’ve seen:
AI is a capability.And like any capability, it needs time, structure, investment, and a map.
If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.
Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”
I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.
For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.
That era is ending.
“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”
At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.
Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.
Transparency Is the New Talent Advantage
Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.
Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.
“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”
What it is: The median time between application and hire.
Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.
Faster time to hire = faster access to revenue-driving talent.
What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.
Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.
And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.
You don’t just measure experience. You understand it in the candidates’ own words.
What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.
Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.
Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.
Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.
What it is: The percentage of completed applications that result in a hire.
Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.
Hiring yield signals funnel health, recruiter performance, and candidate-process fit.
What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.
Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.
Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.
To effectively lead, you need more than simply tracking; you need insights enabling action.
When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.