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
Reinventing the Competency Framework: A Data-Driven Approach for the AI Era
We can’t hide from reality anymore. Talent needs are shifting overnight, and AI is redefining what it means to work. Traditional talent frameworks are no longer fit for purpose. At Sapia.ai, we believe the future of talent strategy lies in a smarter, fairer, and more adaptive way of defining what great looks like.
Our AI hiring platform is built on the largest proprietary dataset of interview answers globally – we’re a data company at heart, and we’ve seen the power of data-driven people methodology in transforming how organisations hire and retain good talent.
So, when it came to building a new Competency Framework that could be leveraged globally for hiring for any role at any scale, of course, we used a ground-up, data-led methodology that bridges the gap between organisational psychology and AI.
Why Rethink Competency Frameworks?
Conventional frameworks are typically crafted through expert interviews and focus groups. While valuable, they tend to be subjective, static, and too slow to keep pace with evolving job demands. As roles become more fluid and technology augments or replaces task-based skills, organisations need a new way to understand the human capabilities that genuinely matter for performance.
We wanted to identify enduring, job-agnostic competencies that reflect what drives success in a modern workplace – capabilities like adaptability, resilience, learning agility, and customer orientation.
(Why competencies and not just skills? Read why here.)
Our Approach: Where AI Meets I/O Psychology
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
- Can we make competency discovery agile, scalable, and evidence-based?
- Can we use AI to automate the process without losing the rigour of traditional psychology?
The answer to both: yes.
We began with a rich dataset of over 37,000 job descriptions across industries and role types. Using large language models (LLMs) and advanced NLP techniques, we extracted over 200,000 behavioural descriptors. These were distilled down through a four-step process:
- Behavioural Descriptor Extraction
- Clustering and Labeling
- Cluster Analysis by I/O Psychologists
- Thematic Categorisation and Definition of Competencies
This resulted in a refined list of 25 human-centric competencies, each with clear behavioural indicators and practical relevance across a wide range of roles.
Built to Scale. Built to Adapt.
Our framework is intelligent, but importantly, it’s adaptive. Organisations can apply this methodology to their own job descriptions to discover custom competencies. This bottom-up, role-data-led approach ensures alignment to real work, not just theoretical models.
And because the framework integrates directly with our AI-powered hiring tools, you get a connected system that brings your talent strategy to life.
Our framework comes to life in the following tools:
- Job Analyser – Starting with a job description, it creates a unique competency profile for each role to build tailored structured interviews in seconds.
- Structured Chat-based Interviews that assess candidates’ responses according to the competency profile for consistent candidate assessment.
- Talent Insights Reports from every interview with deep reasoning and explainability for fair and objective hiring decisions.
- Phai Career Coach for internal mobility and employee growth that considers their competency strengths and career aspirations.
The Future of Talent Acquisition & Development is Competency-First
Skills alone cannot predict success. Competencies do. As AI continues transforming how we work, Sapia.ai’s Competency Framework offers a scalable, scientific, and fair foundation for hiring and developing the talent of tomorrow.
Want to see how it works? Download the full framework.
Blog
It’s Time to Stop Hiring for Skills, and Start Hiring for Competencies
If you’re a CHRO or Head of Recruitment at an enterprise today, chances are you’ve been inundated with messages about the importance of “skills-based hiring.” LinkedIn’s recent Work Change Report (2025) is full of compelling data: a 140% increase in the rate at which professionals are adding new skills to their profiles since 2022, and a projection that by 2030, 70% of the skills used in most jobs today will have changed.
This is essential reading. But there’s a missed opportunity: the singular focus on “skills” fails to acknowledge the real metric that talent leaders need to be using to future-proof their workforce — competencies.
Skills vs Competencies: The Crucial Distinction
- Skills are task-specific capabilities. Think Python programming, Excel, or even negotiation.
- Soft skills refer to interpersonal or behavioural qualities like adaptability, communication, and resilience.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
- Competencies are clusters of skills, knowledge, behaviours and abilities that are observable, measurable, and context-specific.
Put simply, competencies answer the all-important question: Can this person apply the right skills, in the right way, at the right time, to deliver results in our environment?
Why Competencies Matter More Than Ever
The Work Change Report outlines a future where job titles are fluid, roles evolve quickly, and AI is a constant disruptor. This creates three massive challenges for hiring at scale:
- Roles are changing faster than static skill frameworks can keep up
- Job candidates may have non-linear, cross-functional backgrounds
- The shelf-life of technical skills is shrinking rapidly
Skills alone don’t tell us whether someone can succeed in a role that will look different 12 months from now. But competencies can. Because they measure not just what a person knows, but how they apply it.
Adaptive Talent: The New Competitive Advantage
The LinkedIn report highlights a critical insight: organisations now prioritise agility in entry-level hiring. And there’s a good reason for that. With professionals expected to hold twice as many jobs over their careers compared to 15 years ago, adaptability is not just a nice-to-have. It’s core to success.
But you can’t measure agility with a keyword on a CV. You measure it by looking at competencies like:
- Learning agility
- Change resilience
- Cross-functional collaboration
- Problem-solving in ambiguous contexts
When you shift the focus away from skills to behavioural competencies that can be defined, observed, and assessed in structured ways, you open yourself up to a much more dynamic and more useful way of managing talent.
Building a Competency-Based Talent Framework
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
- Define Role-Specific Competencies: Move beyond job descriptions based on qualifications or vague skill sets. Break roles down into measurable competencies that reflect current and emerging performance expectations. This step is crucial for organisations to be able to accurately assess role-fit in the next stages. Sapia.ai does this automatically, taking job descriptions and building role-specific competency models in seconds.
- Assess Competencies Fairly and Objectively: Use structured behavioural interviews, ideally at scale. These provide a much more accurate picture of a candidate’s readiness than self-reported skills or credentials. Sapia.ai’s AI powered interviews enable competency assessment, at scale.
- Build Pathways for Development and Internal Mobility: A competency framework makes it easier to identify transferable strengths, development gaps, and future-fit potential. It gives employees clarity on how to grow within the business. Using an AI-powered coach can help ensure that talent is being continuously developed against the organisation’s competency framework.
The Future of Work Requires Depth, Not Just Breadth
LinkedIn’s data shows that people are learning more skills more quickly than ever. But the real question for talent leaders like you is: Are those skills being applied in ways that drive value? Are we hiring for task proficiency or performance?
The truth is that the organisations that will thrive in an AI-driven, skills-fluid economy aren’t the ones chasing the next hot skill. They’re the ones designing systems to identify, develop and scale competence.
Keen to Shift to Competencies, but Lacking a Framework?
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.
Blog
The AGC Debate: Are AI-Written Interview Answers a Red Flag or Smart Strategy?
Every day, we read stories of increased fake or AI-assisted applications. Tools like LazyApply are just one of many flooding the market, driving up applicant volumes to never-before-seen levels.
As an overwhelmed hiring function, how do you find the needle in the haystack without using an army of recruiters to filter through the maze?
At Sapia.ai, we help global enterprises do just that. Many of the world’s most trusted brands, such as Qantas Group, have relied on our hiring platform as a co-pilot for better hiring since 2020.
Our Chat Interview has given millions of candidates a voice they wouldn’t have had – enabling them to share in their own words why they’re the best fit for the role. To find the people who belong with their brands, our customers must trust that their candidates represent themselves. Thus, they want to trust that our AI is analysing real human answers—not answers from a machine.
The Rise of GPT
When ChatGPT went viral in November 2022, we immediately adopted a defensive strategy. We had long been flagging plagiarised candidate responses, but then, we needed to act fast to flag responses using artificially generated content (‘AGC’).
Many companies were in the same position, but Sapia.ai was the only company with a large proprietary data set of interview answers that pre-dated GPT and similar tools: 2.5 billion words written by real humans.
That data enabled us to build a world-first:- an LLM-based AGC detector for text-based interviews, recently upgraded to v2.0 with 99% accuracy and a false positive rate of 1%. An NLP classification model built on Sapia.ai proprietary data that operates across all Sapia.ai chat interviews.
Full Transparency with Candidates
Because we value candidate trust as much as customer trust, we wanted to be transparent with candidates about our ability to detect artificially generated content (AGC). As an LLM, we could identify AGC in real time and warn candidates that we had detected it.
This has had a powerful impact on candidate behaviour. Since our AGC detector went live, we have seen that the real-time flagging acts as a real-time disincentive to use tools like ChatGPT to generate interview responses.
The detector generates a warning if 3 or more answers are flagged as having artificially generated content. The Sapia.ai Chat Interview uses 5 open-ended interview questions for volume hiring roles, such as retail, contact centre, and customer service, and 6 questions for professional roles, such as engineers, data scientists, graduates, etc.
Let’s Take a Closer Look at the Data…
We see that using our AGC detector LLM to communicate live with candidates in the interview flow when artificial content has been detected has a positive effect on deterring candidates from using AI tools to generate their answers.
The rate of AGC use declines from 1 question flagged to 5 questions – raising the flag on one question is generally enough to deter candidates from trying again.
The graph below shows the number of candidates, from a total of almost 2.7m, that used artificially generated content in their answers.
Differences in AGC Usage Rate by Groups
We see no meaningful differences in candidate behaviour based on the job they are applying for or based on geography.
However, we have found differences by gender and ethnicity – for example, men use artificially generated content more than women. The graph below shows the overall completion ratios by gender – for all interviews on the left and for interviews where the number of questions with AGC detected is 5 or more on the right.
Perception of Artificially Generated Content by Hirers.
We’re curious to understand how hirers perceive the use of these tools to assist candidates in a written interview. The creation of the detector was based on the majority of Sapia.ai customers wanting transparency & explainability around the use of these tools by candidates, often because they want to ensure that candidates are using their own words to complete their interviews and they want to avoid wasting time progressing candidates who are not as capable as their chat interview suggests.
However, some of our customers feel that it’s a positive reflection of the candidate, showing that they are using the tools available to them to put their best foot forward.
It’s a mix of perspectives.
Our detector labels it as the use of artificially generated content. It’s up to our customers how they use that information in their decision-making processes.
This concept of having a human in the loop is one of the key dimensions of ethical AI, and we ensure that it is used in every AI-related hiring product we build.
Interested in the science behind it all? Download our published research on developing the AGC detector 
