To find out how to interpret bias in recruitment, we also have a great eBook on inclusive hiring.
What is workplace diversity?
While workplace diversity might once have been considered a ‘nice to have’, today it’s a ‘must-have’ for employers who recognize the value it brings to their organization, especially in the context of diversity hiring. The core idea of workplace diversity is that the people in any organization’s team should reflect the society in which we live – that is people of different genders, different ages, and different ethnic and cultural backgrounds. That seems logical and simple enough, yet achieving diversity, and especially achieving diversity hiring goals, is still a struggle for many.
What does diversity look like?
Today, workplace diversity is not just about increasing female representation and employing team members from different cultural backgrounds. While these are great goals and are central to many diversity hiring ideas, true diversity is about so much more.
Diversity can be broadly sorted into two categories:
Inherent – effectively the defining traits and characteristics we are born with – gender, ethnicity, sexual orientation, age, socio-economic background, religious and cultural backgrounds.
Acquired – reflecting our experience of the world around us and covering things like education, life knowledge, learned values and skills, socio-economic mobility, political beliefs. These are developed, earned or achieved over time.
It’s the combination of inherent and acquired traits that make people and societies diverse. This holistic view of culture, background, life experience, education, values, and perspectives is a top priority for recruiters and employers alike, emphasizing the need for effective diversity hiring platforms and tools.
What is diversity hiring?
Diversity hiring, often inquired as “what is a diversity hire?”, simply describes the processes of recruiting that support diversity in the workplace. Diversity hiring is not about increasing workplace diversity for the sake of diversity. Hiring for diversity is all about giving every candidate an equal opportunity, regardless of their background. It’s about identifying and removing any steps in the diversity hiring process in sourcing, screening, and shortlisting candidates that may allow discrimination against candidates and personal characteristics that have nothing to do with their ability to do the job such as gender, age, religion, sexual orientation and so on.
By removing biases against individuals or groups of candidates, the process of finding the best candidates to be considered for the role can be based on merit and all the qualities identified as essential for the role and the organisation.
A fairer path to recruitment considers the experience of the candidate at every single step.
From discovering an opportunity through to offer. It addresses bias, inclusivity and fairness. And ideally, it makes recruiters’ lives easier. This is explored in the Inclusive Hiring e-Book here >
Why do you need diversity?
Diversity is embraced by companies who understand the value it brings to their business. Why diversity hiring is important is highlighted by many studies.
In their 2018 report Delivering through Diversity, McKinsey&Company found that:
- Companies in the top-quartile for gender diversity on executive teams were 21% more likely to outperform on profitability and 27% more likely to have superior value creation.
- Beyond gender, companies in the top-quartile for ethnic/cultural diversity on executive teams were 33% more likely to have industry-leading profitability. This diversity included traits such as age and generation, international experience and LGBTQIA+ representation.
While McKinsey’s study was focused on US global companies, their findings are reflected in other studies, white papers and shared experiences of organisations all around the world.
They confirm that workplace diversity impacts a wide range of business metrics:
- better performance and productivity
- business growth
- improved problem-solving abilities
- increased creativity and innovation
- a sense of belonging that boosts employees’ health and wellbeing
- fewer incidents of discrimination or harassment in the workplace
- improved employee retention and tenure
- enhanced reputation as an employer
It’s what employees want too
Unsurprisingly, diversity in the workplace can be a deal maker or breaker for millennial and GenZ job seekers. Deloitte found that 83% of millennials are more engaged when they can know a company fosters an inclusive culture.
But it’s not just the next generations. A recent survey by Glassdoor found that 67% of all candidates say it’s an important factor when considering employment opportunities while more than 50% of current employees want their workplace to do more to increase diversity, emphasizing why is diversity hiring important.
Diversity hiring laws
While there’s no doubt that diversity hiring is good for business, for any organization that doesn’t embrace diversity and hiring practices, the opposite can also be true. Apart from missing out on the benefits that diversity brings to productivity, employee satisfaction, and business reputation, employers also risk breaking the law.
Within Australia, diversity is supported by national and state laws that cover equal employment opportunity, human rights, and anti-discrimination in the workplace. It’s essential that all employers understand their own responsibilities and the rights of employees or job candidates. The cost of non-compliance can be severe while the damage to an organization’s reputation could be matched by irreparable damage to sales, business contracts, and their employer brand.
In Australia, it is unlawful to disadvantage employees and job seekers in any way because of their:
- race
- colour
- gender
- sexual orientation
- age
- physical or mental disability
- marital status
- family or carer’s responsibilities
- pregnancy
- religion
- political opinion
- national extraction (place of birth or ancestry)
- social origin (class, caste or socio-occupational category)
- industrial activities (such as belonging to a trade union)
Unconscious bias – it’s a human condition
Whether innate or learned, everybody is capable of unconscious bias. Reinforced by our own personal experiences, cultural background, beliefs and world view, bias is how we feel about something – a person or group of people, an idea, a thing – and how we use those feelings to make judgements and decisions about those people or things, often instantaneously.
Psychologists and researchers have identified over 150 types of bias that impact the way we engage, assess and interact with others. In the recruitment process that’s 150 ways that otherwise suitably qualified candidates can be overlooked, ignored, put aside or deliberately discounted. You can read more about unconscious bias in our article here.
Algorithms do the job humans can’t
Because unconscious bias is a universal and inherently human condition, it’s a problem that can’t be solved by any amount of bias training or awareness.
So if humans can’t solve the very human problem, what can be done? Sapia has solved the issue of unconscious bias in hiring by taking humans out of the process for top-of-funnel interview screening through an Artificial Intelligence enabled chat interview platform. It’s an easy way to implement data-driven decision-making with a structured and automated process that provides a level playing field for all candidates.
Six more ways to build your diversity hiring capabilities
Adopting Sapia Ai-enabled decision-making to remove bias from the early interview process is one of the easiest ways to get diversity hiring working for you. Here are some further ideas from Sapia’s team to help increase diversity in candidate sourcing, screening, and, ultimately, hiring.
1.Agree your diversity hiring goals
More female graduates in technical roles? A better cultural spread across the organization? More women in middle management? Without understanding how diversity hiring supports your business plans, how would you ever know you’re making progress? Diversity hiring strategies and initiatives should be agreed by your leadership team, documented in HR plans, and socialized among all stakeholders.
2. Develop your employer brand and policies that support diversity
Developing a reputation as an employer who values and nurtures diversity starts with your own people. Talk to your people to hear what’s important to them and understand if they think any policies (or attitudes) are holding diversity back. Talk to your team about diversity and the benefits it can bring.
Think about policies that may support more diversity in your workplace. Beyond hiring, it may be providing extra time off for community events or religious festivals, or simply providing workplace flexibility and freedom for employees to be comfortable being themselves.
The more your team buy into policies that support, value, and celebrate diversity, the more your reputation as a diversity employer will organically grow. And the more it grows, the easier diversity hiring will become… as candidates who value diversity will be lining up to work with you.
3. Use your ATS to build diverse talent pools
Sapia’s automated interview platform is designed to integrate seamlessly with leading Applicant Tracking Systems (ATS). Even before the interview process, use screening tools in the ATS to filter and sort candidates on skills, qualifications or experience alone. This blind screening to identify candidates with the best potential adds an additional layer of bias-free screening to your diversity hiring.
4. Mind your language and rethink your screening factors for diversity
Undertaking a review of past job ads can help you see where bias may have crept into your recruiting process. Is your language inclusive? Would all candidates feel they could apply regardless of age, gender or cultural background? While being careful not to actually be biased, your words can talk more directly to the candidates you want to attract and explain why they’d be a great fit for your team.
While you’re reviewing the way you reach out to candidates, also consider whether you’re screening or interviewing for the qualities you actually value most or you’re unconsciously guiding the process towards certain types or profiles. Sometimes you need to ask others to check your own bias.
5. Add some diversity into your candidate sourcing
Is it time to fish for candidates in a different talent pool? If you’re relying on the same sources and same screening factors, you’re likely to keep cultivating the same type of candidate. Think about where and how you can connect with a more diverse candidate pool.
If you are targeting more women in specific roles, for example, find relevant interest or networking groups online or within platforms such as LinkedIn and talk to candidates directly. Ask your female employees to recommend their own connections or former colleagues and share job leads. The same principle applies to reaching out to any particular demographic or skill set and employees appreciate having their opinions and recommendations heard and valued.
6. Consider some affirmative action
Especially when you’re starting your diversity hiring journey, you may want to help things along with specific diversity programs that could offer an internship or traineeship to candidates of specific backgrounds. Consider working with local schools, colleges, or community groups to make connections and target the appropriate up-and-coming candidates. It can also be a great way to engage and motivate your own team in supporting diversity hiring goals.
Sapia: Blind screening at its best
Sapia solves bias by screening and evaluating candidates with a simple open, transparent interview via a text conversation.
Candidates know text and trust text and questions can be tailored to suit the requirements of the role and the organisation’s brand values. Unlike competitors, Sapia has no video hookups, visual content or voice data. No CVs and no data extracted from social channels. All of which can be triggers for bias– unconscious or otherwise.
Sapia’s solution is designed to provide every candidate with a great experience that respects and recognises them as the individual they are. People are more than their CV and candidates appreciate the opportunity to tell their story in their own words, in their own time. Sapia is the only conversational interview platform with 99% candidate satisfaction feedback. You can read more about blind screening in our article here.
Find out more about Sapia’s AI-powered interview automation platform and how we can support your diversity hiring goals.
You can try out Sapia’s Chat Interview right now – here – or leave us your details to get a personalised demo
Related
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:
Blog
New Research Proves the Value of AI Hiring
A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.
Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.
We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.
The problem with traditional psychometric tests
While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.
For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.
This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.
Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.
The Rise of Chat-Based Interviews
Enter conversational AI.
Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach.
Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.
The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.
Importantly, every candidate receives the same questions, evaluated against the same objective, explainable framework. These interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.
Key Findings from the Latest Research
The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.
One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.
The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.
And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.
Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.
Why Trust and Candidate Agency Win
Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.
Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.
This signals respect from the employer. It says: We trust you to show us who you are.
Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.