Organisational diversity is still an issue, and the cost is real.
You would think in this day and age organisational diversity would be a moot point. With global social reforms across gender, sexuality, disability and race equality, one could believe the challenge of diversity has been overcome.
Sadly, this is not the case. Some fu(cked) facts:
- In 2018, unemployment of ethnic minority groups in the United States made up 44.9% of total unemployment.The worst-off being Black or African American, despite the fact they represent less than 14% of the total population.[1]
- In Australia, the average full-time weekly wage for a woman is 15.3% less than a man; and women will retire with less than half the superannuation of a man.[2]
- In the UK, men with physical impairments generally experience pay gaps in the range of 15% to 28%, depending on the nature of the disability. The difference between non-disabled women’s pay and that of women with physical impairments ranges from 8% to 18%.[3]
- In the United States, 42% of Transgender American’s are unemployed and 31% are living at the poverty level.[4]
So why do we continue to see inequality in employment?
Despite all the awareness and work to improve employment equality and inclusion, there are four commons slip-ups in recruitment which allow for underlying bias to be introduced into the hiring process.
1. Job descriptions
Despite the best of intentions, hiring managers or recruiters can discourage groups of potential applicants. They do so by using restrictive terms which are gendered or ageist. This can extend to unnecessary education standards which are not required to do the role.
2. CV filtering
More often than not, recruiters and hiring managers are overwhelmed with application volumes. To save time CV screening is done for job titles, big brand company names, and favouring certain universities or education providers.
3. Unconscious name filtering
In some instances, unintentionally or intentionally, applicants will be filtered out of the screening process based on their name. Researchers of Harvard and Princeton found that blind auditions increased the likelihood that female musicians would be hired by an orchestra by 25 to 46%. Whilst one seminal study found that African American sounding names had a 50% lower call back rate for an interview when compared with typical White named individuals.
4. All of the unconscious biases
Would you believe there are over 100 different forms of cognitive biases? Confirmation bias, affinity bias, similarity bias, halo effect, horn effect, status quo bias, conformity bias… the list goes on. These biases make diverse hiring an even more difficult process as you don’t even know that you are missing out on the best candidates!
The Bottom Line? Lack of diversity costs.
Time and time again research has shown that diverse organisations are more effective, perform better financially and have higher levels of employee engagement.
A recent McKinsey report, “Delivering through Diversity” showed that organisations with gender-diverse management were 21% more likely to experience above-average profits. Whilst companies with a more culturally and ethnically diverse executive team were 33% more likely to see better-than-average profits. This figure grows to 43% when the board of director level is also diverse in gender, ethnicity, sexual orientation.[5]
More compelling is that for every 1% rise in workforce gender and cultural diversity, there is a corresponding increase of between 3 to 9 per cent in sales revenue![6]
Not only is diversity a social and ethical problem for organisations, but it is also a commercial one.
Assessing all applicants blindly and equally leads to improved diversity, recruitment efficiency, and organisational performance.
Blind screening: Removing information that reveals the candidate’s race, gender, age, names of schools, etc to reduce unconscious bias that creeps into hiring decisions.
For our customer, a global airline, cabin crew are at the heart of delivering great customer experience. With 9000+ cabin crew creating iconic experiences for passengers every day, they want to maintain their strong brand. They intend to do this through hiring the best in customer service to give their applicants an iconic experience.
An iconic brand also attracts an enormous number of applications some of which don’t fit the criteria. Sifting through so many CVs to uncover the right candidates is extremely time-consuming for the recruiters.
Some of the challenges the team faced in their existing processes included:
- Using videos to screen applicants that only added to the cognitive and time load for recruiters and introduced the opportunity for bias.
- Low yield from assessment centres for those applicants shortlisted from screening.
- Maintaining a gender mix in the applicant pool was important. Yet their screening tools were inadvertently omitting men from the process.
The results were amazing.
- No bias in the process with the ratio of men and women unaffected leading to a more diverse set of hires in comparison to prior campaigns.
- Shortlisting completed in 2 hours instead of 2 weeks.
- Over 2.5 times more offers were made compared with the existing process. 100% completion of the process by applicants.
- Average candidate enjoyability score of 9/10.
A post-campaign survey showed a perfect score from the recruitment team rating the technology as faster, fairer and delivering better candidates.
How conversational AI can truly remove bias in screening
No matter the good intentions, humans will always lean on their biases when making decisions. Interrupting bias in recruitment needs a systemic solution. Something that can operate independently, in the absence of a human trusted to do the right thing.
While Sapia does not claim to completely solve for bias within an organisation, using a chat-based assessment at the top of the recruitment funnel will help you to interrupt, manage and therefore change, biases that reduce diversity in hiring.
A fair and inclusive Candidate Experience
Chat is inclusive for all candidates
Candidates chat through text every day. It’s natural, normal and intuitive. Chat interviews provide an opportunity for them to express themselves, in their way, with no pressure.
Playing games to get a job is not relevant. Talking to a camera is not fair. What if you are unattractive, introverted, not the right colour or gender, or don’t have the right clothes? When you use chat over other assessment tools, you’re solving for adoption, candidate satisfaction, inclusivity and fairness. Our platform has a 99% candidate satisfaction score, and a 90% completion rate. Here’s the 2020 Candidate Experience Playbook.
We use an intrinsically blind assessment design
Blind screening means an interview that is truly blind to the irrelevant markers of age, gender and ethnicity. That just can’t see you. And therefore, cannot judge you. Sapia does not use any information other than the candidate responses to the interview questions to infer suitability for the job your candidates are applying for. As a company we call this ‘fairness through unawareness’. The algorithm knows nothing about sensitive attributes and therefore cannot use them to assess a candidate. Sapia only cares if the candidate is suitable for the job, and nothing else.
Why is organizational diversity important?
Are there some examples of organizational dimensions of diversity?
What does diversity mean?
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References
[1] https://builtin.com/diversity-inclusion/diversity-in-the-workplace-statistics
[2] https://humanrights.gov.au/
[3] https://www.equalityhumanrights.com/en/publication-download/research-report-107-disability-pay-gap
[4] https://theconversation.com/transgender-americans-are-more-likely-to-be-unemployed-and-poor-127585
[5] https://www.forbes.com/sites/pragyaagarwaleurope/2018/10/19/how-can-bias-during-interviews-affect-recruitment-in-your-organisation/#79b1c0b81951
[6] https://www.forbes.com/sites/pragyaagarwaleurope/2018/10/19/how-can-bias-during-interviews-affect-recruitment-in-your-organisation/#79b1c0b81951
Related
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.
Blog
AI Maturity in the Enterprise
Barb Hyman, CEO & Founder, Sapia.ai
It’s time for a new way to map progress in AI adoption, and pilots are not it.
Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.
And here’s what struck me:
Despite the hype, we still don’t have a shared language for AI maturity in business.
Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.
You don’t need more pilots. You need a maturity model.
The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.
It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.
Here’s an early sketch based on what I’ve seen:
The 5 Stages of AI Maturity (for real enterprises)
- Curious
- Awareness is growing across leadership
- Experimentation led by innovation teams
- Risk is unclear, appetite is cautious
- AI is seen as “tech”
- Reactive
- Gen AI introduced via vendors or tools (e.g., copilots, agents)
- Some pilots show promise, but with limited scale or guardrails
- Data privacy and sovereignty questions begin to surface
- Risk is siloed in legal/IT
- Capable
- Clear policies on privacy, bias, and governance
- Dedicated AI leads or councils exist
- Internal use cases scale (e.g., summarisation, scoring, chat)
- LLMs integrated with guardrails, safety reviewed
- Strategic
- AI embedded in workflows, not layered on
- LLM/data infrastructure is regionally compliant
- AI outcomes measured (accuracy, equity, productivity)
- Teams restructured around AI capability — not just tech enablement
- AI-Native
- AI informs and transforms core decisions (hiring, pricing, customer service)
- Enterprise builds proprietary intelligence
- FAIR™/RAI principles deeply operationalised
- Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders
AI is a capability.And like any capability, it needs time, structure, investment, and a map.
If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.
Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”
I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.
Blog
Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever
For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.
That era is ending.
“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”
At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.
Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.
Transparency Is the New Talent Advantage
Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.
Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.
“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”
Metrics That Make Transparency Real (and Actionable)
🕒 Time to Hire
What it is: The median time between application and hire.
Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.
Faster time to hire = faster access to revenue-driving talent.
💬 Candidate Sentiment, Advocacy & Verbatim Feedback
What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.
Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.
And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.
You don’t just measure experience. You understand it in the candidates’ own words.
🔍 Drop-Off Rates, Funnel Visibility & Automation That Works
What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.
Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.
Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.
Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.
📈 Hiring Yield (Hired / Applied)
What it is: The percentage of completed applications that result in a hire.
Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.
Hiring yield signals funnel health, recruiter performance, and candidate-process fit.
🧠 AI Effectiveness: Score Distribution & Answer Originality
What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.
Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.
Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.
From Metrics to Momentum
To effectively lead, you need more than simply tracking; you need insights enabling action.
When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.