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.
Blind Hiring.
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’.
It’s hard to admit our biases play a role in our decisions. That’s why Blind Hiring works.
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.
The science is in. People are biased. And, again, that’s why Blind Hiring works.
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.
- German-sounding Sandra received interview invitations 19% of the time.
- The identical application from Meryem recieved a call back 14% of the time while Meryem with a headscarf got called just 4% of the time.
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.
Tackling bias in hiring & recruitment
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?
A closer look at blind hiring
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:
Blind Screening
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.
Blind Assessments
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.
Blind Interviewing
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
Blind interviewing, testing and screening in one – how Sapia has changed the game
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.
The ultimate in blind 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.
Removing bias elements from the equation
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.
Algorithms see what humans can’t
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.
Blind hiring is better for business
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 …
- Blind screening
- Using inclusive and engaging technology
- Apply a consistent assessment rubric
- Explainable and relatable process
- Relying only on data consensually given by the candidate
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
Have you seen the New Inclusive e-Book?
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:
- How to design an inclusive recruitment path. From discovery to offer and validation of the process.
- The hidden inclusion challenges that are holding your organisation back.
- How to tell if Ai technology is ethical.
Related
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.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: