To find out how to interpret bias in recruitment, we also have a great eBook on inclusive hiring.
And then suddenly the video interview went mainstream!
Whether it’s Google Meet, Facetime or Zoom, 2020 will always be remembered as the year that video meet-ups went mainstream. It’s how kids kept up their lessons. How their parents hooked up with their personal trainers. It’s where people met up for Friday drinks. And of course, it’s the technology that enabled millions to stay connected to colleagues and clients while working from home.
And just as video has impacted so many parts of our lives and businesses, it also accelerated the adoption of video tools in contemporary recruiting.
It might be considered the next-best-thing to ‘being there’, but could video interviewing actually be filled with traps that are working against the best interests of recruiters, candidates and employers?
What is a video interview?
There are two types of video interviews:
- one-way or asynchronous video interviews – where candidates record their responses to a set of job-relevant questions.
- two-way video interviews – using one of the platforms described above or bespoke tools that connect the interviewer (or interviewing panel) in conversation with candidates.
Can video interviews really reduce unconscious bias?
Within both types of video interviews, an ability to reduce unconscious bias is promoted as a key benefit.
Unconscious bias is the sum of the inherent beliefs, opinions, cultural background and life experiences that shape how we assess, engage and interact with others.
There are several ways that video interviewing might help reduce unconscious bias:
- A consistent experience – With a structured approach to interview questions and process that provides every candidate with the same parameters. A standardised experience for every candidate can be seen to reduce bias. When questions are set, there’s little or no room for distracting small talk (in two way interviews) that may reveal bias triggers.
- No geographic or travel barriers – By interviewing all candidates in a location of their choosing, the bias of distance and the effort and expense of travel to attend an interview in person is reduced.
- Open the opportunity to more candidates – With the ability to automate video interviews and applications, recruiters can connect with many more candidates, helping to reduce the bias that may see a CV or application ignored or put aside.
The bias problem that’s staring you in the face.
As much as proponents of video screening or interviewing claim it removes bias from the process, by its very nature, the opposite is in fact true.
As soon as an interviewer or hirer sees a candidate, the blindfolds of bias are removed. No matter how aware or trained in bias the reviewers may be, images and sound can trigger bias. Additionally, it can distract attention from the things that really matter. Here are just a few things that someone talking to the camera will reveal. All possible points of unconscious bias:
- gender
- age
- skin colour
- cultural background
- visible disabilities
- attractiveness or otherwise
- what people wear – headscarves, religious jewellery, or maybe you just don’t like stripes or the candidate’s personal style
- the background of the video – are you making judgements about candidates because of their home environment or choice of art on the walls
- accents might sound ‘funny’ or strange to your ear
- candidates may have unusual voices or speech impediments that would not impact their ability to perform in the role
- you may negatively associate candidates with other people you’ve worked with or met
- the candidate may be highly nervous about ‘performing’ for the camera, affecting their ability to speak normally and communicate clearly
No rule says you need to see someone to hire them
That’s just a bias (much like the bias pre-Covid) that you need to see someone at work to know that they are doing the work.
Blind hiring means you are interviewing a candidate without seeing them or knowing them. It’s fair for the candidate and also smart for your organisation.
If you are hanging your hat on the fact you just finished bias training- research has shown consistently unconscious bias training does not work.
While we have all been dutifully attending it for years, the truth is the change factor is zero.
Video interviews vs text interviews. Which delivers blind interviewing at its best?
Sapia’s 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.
Unlike video interviewing, Sapia removes all the elements that can bring unconscious bias into play – video, visual content such as candidate photos or data gathered from social channels such as LinkedIn. Sapia even takes CVs out of the process.
Read: The Ultimate Guide To Interview Automation With Text-Based Assessments
An enjoyable and empowering candidate experience
While being ‘camera shy’ works against many candidates in video interviews, Sapia evaluates candidates with a few simple open, transparent questions via a text conversation.
Candidates know text and are comfortable using it. A text interview is non-threatening and candidates tell us they feel respected and recognised as the individual they are. They are grateful for the space and time to tell their story in their words. It’s the only conversational interview platform with 99% candidate satisfaction feedback.
Better hiring outcomes with Sapia
Beyond a more empowering candidate experience, the platform helps recruiters and employers connect with the best candidates faster and cost-effectively. The platform uses Ai, machine learning and NLP to test, assess and rank candidates according to values, traits, personality, communications skills and more.
Recruiters can gain valuable personality insights and the confidence of a shortlist with the best matched candidates to proceed to live interviews. By removing bias from the screening process Sapia is helping employers increase workplace diversity.
Does video hiring productise bias?
In recent years, we have all wisened up to the risk of using CVs to assess talent. A CV as a data source is well known to amplify the unconscious biases we have. A highly referenced study from 2003 called “Are Emily and Greg More Employable than Lakisha and Jamal?” found that white names receive 50 per cent more callbacks for interviews.
However, during COVID, we reverted to old ways in a different guise.
HR substituted CV as a data input with video interviews.
This isn’t a step forward.
Video hiring productises bias. It actually enables bias at scale.
It leads to mirror hiring – those who look and sound most like me. Instead of screening CVs in 30 seconds now, your team is watching 3-minute videos, so recruiting takes longer, and it’s exhausting.
Video platforms are being challenged in the US (EPIC Files Complaint with FTC about Employment Screening Firm HireVue) for concerns over invisible biases that may be affecting candidate fairness given the opaque nature of those algorithms. Facial recognition systems are worse at identifying the gender of women and people of colour than at classifying male, white faces. This year IBM openly pulled out of facial recognition, fearing racial profiling and discriminatory use, partly due to the questionable performance of the underlying AI.
How did we substitute one inferior and biased methodology with another that’s arguably even more biased?
We get that at some point you and the candidate need to meet, although no rule says you need to see someone to hire them. That’s just a bias (much like the bias pre-Covid) that you need to see someone at work to know that they are doing the work.
Blind hiring means you are interviewing a candidate without seeing them or knowing what school they went to, the jobs they have had. It’s a real meritocracy in that it’s fair for the candidate – and also smart for your organisation.
If you are hanging your hat on the fact you just finished bias training- research has shown consistently unconscious bias training does not work.
While we have all been dutifully attending it for years, the truth is the change factor is zero.
At a recent event attended by academics and data-loving professionals –whilst there was a welcome recognition that humans are more biased than Ai, and despite hearing that Wikipedia lists more than 150 biases we humans have – the majority of the audience still believe the impossible: that we can be trained out of our unconscious biases.
Algorithms are better at dealing with biases
The Nobel Prize winner Dr Daniel Kahneman prescribes an algorithmic approach as better at decision-making to remove unconscious biases. He claims “Algorithms are noise-free. People are not. When you put some data in front of an algorithm, you will always get the same response at the other end.” Also, see why machines are a great assistive tool in making hiring a fair process, here.
We know your inbox is flooded with Ai tools with each proclaiming to remove bias and give you amazing results and it’s tough to discriminate between what’s puffery, what’s real and what you can trust.
If your role requires you to know the difference between puffery and science, then read this. Buyers Guide: 8 Questions You Must Ask.
The 30-second due-diligence test that every HR professional should be asking when presented with one of these whizz-bang Ai tools is this:
- No data scientists in the team = not likely to be based on Ai
- No research available even under NDA to substantiate the method of assessment being used = pseudoscience or science that’s flawed if the company is not prepared to share it
- No regular bias testing to review = the Ai is likely to be biased in application
- Data used to training the models is 3rd party/ social media data = high risk of bias.
It’s critical, in fact, it’s a duty of care you have to your candidates and your organisation to be curious and investigate deeply the technology you are bringing into the organisation.
We have to be careful not to think that all AI is biased. AI is based on data, and that data can be tested for bias. ‘Data-driven’ also means transparent. Testing for bias, fairness and explainability of AI models is an active area of research and has advanced a lot. If built with best practices, AI can be used to challenge human decisions and interrupt potential biases. In the end, hiring is a human activity, and the final outcome should always be owned by a human.
Find out more about Sapia’s Ai-powered text interview platform. Also, see how we can support your best-practice recruitment needs today.
To keep up to date on all things “Hiring with Ai” subscribe to our blog!
Finally, you can try out Sapia’s Chat Interview right now HERE >
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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 👇