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Video Interviewing Bias: Problems, Advantages and Disadvantages

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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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. 

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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.

 

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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:

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