Interview bias training is essential for businesses looking to optimize their hiring processes. Is unconscious bias in recruitment holding your business back? When aiming to expand your team, it’s tempting to select a candidate who appears to be a solid ‘cultural fit’.
However, what if that means you’re overlooking a candidate who could be an invaluable ‘cultural add’? Interview bias training can help hiring managers and teams recognize and overcome these pitfalls. When you strive to challenge unconscious bias and foster an environment that appreciates diversity – in terms of background, experience, worldview, and many other facets – you nurture an office that benefits not only your team but also your enterprise.
Hiring based on a gut instinct that someone will mesh well with the team might be a sign that your choice was swayed by unconscious hiring bias. It’s not unusual, and in reality, we all possess unconscious bias and are influenced by it. This is where interview bias training becomes pivotal.
Bias might be evident in others.
You may notice it in how someone behaves or speaks about others. Maybe you’ve felt the sting of bias firsthand. Recognizing our inherent biases can be tough, which is why it’s dubbed unconscious.
Unconscious bias training for recruiters and unconscious bias training for hiring managers is not just a trending term; it’s a burgeoning industry. In this piece, we delve into the pivotal questions surrounding bias: What exactly is unconscious bias? How does it alter the hiring framework? Is it possible to truly counter unconscious bias? If you’ve swiftly reached your own verdicts on these queries, that’s a manifestation of unconscious bias too!
The Imperative Discussion on Bias
From the era when our ancestors congregated around fires, bias has been prevalent.
It’s essentially how we lean towards or against a concept, object, individual, or group. Bias often implies these sentiments are prejudiced or discriminatory.
Bias revolves around presumptions, stereotypes, or trepidation of the unfamiliar. It can be intrinsic or acquired, and unconscious bias is shaped and amplified by our personal histories, cultural backdrop, and surroundings. Biases can be trivial – like despising broccoli – or they can be significantly detrimental.
Why is Unconscious Bias Crucial During Hiring?
The aim of overcoming bias at work is to establish a milieu where every staff member feels the environment is congenial, secure, and devoid of discrimination or harassment. While this might sound idealistic, diverse and inclusive work settings can elevate employee contentment, augment engagement and efficiency, and bolster your company’s repute as an exemplary employer. It also diminishes the risk of potential legal repercussions from inequitable employment practices.
The Most Frequent Forms of Unconscious Bias in the Workplace
Regarding recruitment, certain biases are more prevalent. While some are self-explanatory – such as gender bias, ageism, and racism – experts have pinpointed over 150 types of unconscious bias influencing our interactions. We’ll examine a handful here. It’s plausible you’ve allowed one or more of these biases to sway your decisions, hence missing an ideal candidate.
- Confirmation Bias – Rapidly forming an opinion based on a singular detail and subsequently seeking to validate that impression.
- Overconfidence Bias – Overestimating one’s judgment capability, often intertwined with confirmation bias.
- Illusory Correlation – Misinterpreting or overstating the relevance of certain responses to the candidate’s competence.
- Beauty Bias – Preferring candidates based on appearance, which isn’t indicative of their job proficiency.
- Conformity Bias – Yielding to group consensus even if personal opinions differ.
- Contrast Effect – Comparing candidates to their predecessors instead of evaluating them based on the job’s demands.
How are you scoring in bias roulette?!
Here’s some more:
Affect heuristics – this unconscious bias sounds very scientific, but it’s one that’s being a very human survival mechanism throughout history. It’s simply about making snap judgements on someone’s ability to do a job based on superficial and irrelevant factors and your own preconceptions – someone’s appearance, tattoos, the colour of their lipstick.
Similarity attraction – where hirers can fall into the trap of essentially hiring themselves; candidates with whom they share similar traits, interests or backgrounds. They may be fun to hang out with, but maybe not the best match for the job or building diversity.
Affinity bias – so you went to the same school, followed the same football team and maybe know the same people. That’s nice, but is it really of any relevance to the hiring decision?
Expectation anchor – where the hirer is stuck on what’s possibly an unrealistic preconception of what and who the candidate should be
Halo effect – Your candidate is great at one thing, so that means they’re great at everything else, right? Judging candidates on one achievement or life experience doesn’t make up for a proper assessment of their qualifications and credentials
Horn effect – It’s the devil’s work. The opposite of the halo effect where one negative answer or trait darkens the hirer’s judgement and clouds the assessment process.
Intuition – going with that gut feeling again? While the emotional and intellectual connection may come into the process, it’s largely irrelevant. Focus on their actual experience and capabilities instead.
Can unconscious bias be eliminated? Can bias be unlearned?
In an ideal world, every hire would be approached in an objective way, free of unconscious basis and based on the candidate’s ability to do the job well. However, we don’t live in that perfect world and, time and time again, bias can cloud our judgement and lead to the wrong recruitment decisions. So what can we do? Let’s first talk about what doesn’t work.
Why unconscious bias training does not work
The efforts of any business to drive affirmative change in their business are to be respected. However, there’s a very good reason why unconscious bias training simply can’t work. Why?
Because unconscious bias is a universal and inherently human condition. Training targets individuals and their well-worn attitudes and world views.
While awareness and attitudes may change, inherent bias will remain because that’s the human condition.
So if humans can’t solve a very human problem, what can? Sapia is challenging the issue of unconscious bias in hiring by promoting ‘top-of-funnel’ screening that entirely avoids humans and their bias. Instead, candidates are interviewed and assessed through automation and algorithms. The data that trains the machine is continuously tested so that if ever the slightest bias is found, it can be corrected.
According to an Article Published By Fast Company:
(Ref. https://www.fastcompany.com/90515678/science-explains-why-unconscious-bias-training-wont-reduce-workplace-racism-heres-what-will)
From a scientific perspective, there are reasons to be cautious that unconscious bias training will have a significant impact on racism, sexism, and other forms of workplace discrimination.
1. MOST BIASES ARE CONSCIOUS RATHER THAN UNCONSCIOUS
Contrary to what unconscious bias training programs would suggest, people are largely aware of their biases, attitudes, and beliefs, particularly when they concern stereotypes and prejudices. Such biases are an integral part of their self and social identity.
2. THERE IS ONLY A WEAK RELATIONSHIP BETWEEN ATTITUDES AND BEHAVIORS
Contrary to popular belief, our beliefs and attitudes are not strongly related to our behaviours. There is rarely more than 16% overlap (correlation of r = 0.4) between attitudes and behavior, and even lower for engagement and performance, or prejudice and discrimination.
3. THERE IS NO ACCURATE WAY TO MEASURE UNCONSCIOUS BIAS
The closest science has come to measuring unconscious biases is via so-called Implicit Association Tests (IAT), like Harvard’s racism or sexism test. (Over 30 million people have taken it, and you can try it for free here. These have come under significant academic criticism for being weak predictors of actual behaviours. For example, on race questions (black vs. white), the reported meta-analytic correlations range from 0.15 to 0.24.
4. IT’S HARD TO CHANGE PEOPLE’S BELIEFS, ESPECIALLY WHEN THEY DON’T WANT TO
The hardest thing to influence through any D&I initiative is how people feel about concepts such as gender or race. Systematic reviews of diversity training concluded: “The positive effects of diversity training rarely last beyond a day or two, and a number of studies suggest that it can activate bias or spark a backlash.”
Algorithms do the job humans can’t
Using machines and artificial intelligence to augment and challenge decisions is fast becoming mainstream across many applications and industries. To reduce the impact of unconscious bias in hiring decisions, testing for bias and removing it using algorithms is possible. With humans, it’s not.
Sappia tackles bias by screening and evaluating candidates with a simple open, transparent interview via a text conversation. Candidates know text and trust text.
Unlike other Ai Hiring Tools, Sapia has no video hookups and no visual content. No CVs.
All of these factors carry the risk that unconscious bias can come into play. Nor is data extracted from social channels as our solution is designed to provide every candidate with a great experience that respects and recognises them as the individual they are.
A better experience for candidates, recruiters and clients alike
A research study by The Ladders found that recruiters only spend about 6 seconds looking at a resume. With bulk-hiring, it’s probably less. That’s 6 seconds to make or break a candidate’s hope.
Sapia’s AI-based screening comes into to its own with high volume briefs, with the capability to conduct unlimited interviews in a single hour/day, assessing >85 factors – from personality traits to language fluency and other valuable talent insights. Candidates receive personalised feedback, coaching tips for their next interview and faster decisions on their progress in the hiring process.
Sapia is not out to replace human recruiters but we are here to work as your co-pilot, helping you to make smarter, faster and unbiased hiring decisions.
Understand where unconscious bias has held your business back
AI-enabled enabled interviewing and assessment also tracks and measures bias at a micro level so businesses can understand the level and type of bias that may previously have influenced decisions. With candidate and client satisfaction rated 95%+, it’s a game-changer for changing behaviours.
Hiring’s a team sport and we’re rewriting the rules
The ability to measure unconscious bias is just one more reason to use AI-based screening tools over traditional processes.
Everyone has a story that’s bigger than their CV.
Sapia gives every candidate an opportunity to tell theirs. Through our engaging, non-threatening process where unconscious bias can be taken out of the equation (literally!), we will help you get to the best candidates sooner.
You’ll get a shortlist of candidates with the right traits and values for your business so you can move ahead to interviews with confidence and clarity. With time and resources saved on upfront screening, your team can concentrate on making the interviewing stage more rewarding for hirers and candidates alike.
With Sapia, you can soon be on your way to building more diverse, inclusive and happier workplaces. We know we can work for your business, so we’d love to work with your business. Let’s talk.
Have you seen the Inclusive e-Book? It offers a pathway to fairer hiring in 2021.
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.
Download Inclusivity Hiring e-Book 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.
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: