More money is flowing into Environmental, Social and Governance (ESG) than ever. In 2021, investors poured $649 billion into ESG-focused funds worldwide, up 90% from the $542 billion invested in 2020. In the UK, over 21% of investors plan to back funds and companies with comprehensive ESG strategies by 2025. And in Australia, more than 55% of super funds are using responsible investment approaches to inform strategic asset allocation.
All this investment has prompted a sharper focus on social issues across major companies – the S in Environmental, Social, and Governance. The great news is that investment in the big S, in turn, means more money and attention toward progress in Diversity, Equity and Inclusion (DEI).
Executives who underscore the significance of diversity in hiring understand that an impactful DEI strategy must originate from the highest ranks – consider the Australian superannuation fund HESTA and its 40:40 vision as a prime example. However, for the strategy to be truly effective, diversity hiring ideas need to permeate all levels of the organization. It’s also critical to meticulously track and measure the extent to which we are achieving our diversity hiring goals to ensure real progress is made.
Both boards and shareholders want measurable change in DEI, and fast. According to a Harvard Business Review study of S&P 500 earnings calls, the frequency with which CEOs talk about issues of equity, fairness, and inclusion has increased by 658% since 2018. The momentum is clear, and expectations are that this will only increase further in the coming years.
Diversity goals need to be measurable, today
According to another HBR article, 40% of US companies discussed DEI in their Q2 2020 earnings calls, which is a huge step up from the 4% of companies that did the year before. And with 1,600 CEOs pledging to take action on DEI, setting goals and tracking progress remain top priorities.
DEI and ESG are big challenges, and we might take myriad possible approaches in trying to solve them. Some companies may start at the executive level (HESTA, as an example), while others may invest in partnerships and outreach programs. The spectrum of options can easily become overwhelming.
| “Interestingly, I’m just looking at our workforce profile and have been discussing the changes in diversity since we updated our recruitment approach last March. Not only have we hired three times more ethnic minorities and 1.5 times more women, but we now have twice as many LGBTQI+ colleagues in our business than we did three years ago! Other initiatives have played a part, but I’d imagine the game changer has been Sapia as we’ve had some direct feedback from a transgender colleague that they felt more confident with our recruitment process than they did in other applications!
David Nally, HR Manager, Woodie’s UK |
So why not start with the people you bring into your company, at all levels? Why not begin with the way you attract, assess, and select talent?
With advanced conversational Ai, you can set realistic DEI targets and measure them comprehensively, ensuring access to the best talent from diverse backgrounds. A sophisticated Interviewer is not just another chatbot that operates on a fixed set of rules. For instance, our conversational Ai delves deep into interview responses to understand each candidate’s unique attributes in a fair and objective manner.
Our Smart Interviewer helps you track and meet these three key diversity goals.
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Gender bias
Our proprietary interview response database is made up of more than 500,000,000 words, enabling us to conduct the most sophisticated response analysis in the recruitment industry. We can do this on a macro scale (e.g. across countries, cultures, industries, and role types); or for individual companies.
Take these findings, combining data from a range of our customers, globally:
Figure 1: Gender stats across applicants, Ai recommendations and hired
Thanks to our machine-learning capabilities, and the vastness of our database, we can provide the hiring team with real-time analytics on the following diversity hiring goals:
- Number of observed female applicants vs number of expected female applicants (and the same for male applicants)
- Diversity smart goals examples such as the overall hiring rate of females vs males
- Overall rate of female vs male recommendations made by our Ai to meet diversity targets
By employing a smart interviewing Ai at the first stage of recruitment, we can prove progress with regards to inclusivity and bias reduction. These aggregate company data show that while the expected number of female applicants exceeded the number of those that actually applied, the number of recommendations made by our Smart Interviewer also beat expectations (effectively compensating for the top-of-funnel bias). We can also see that the rate of observed female hires far exceeded the expected number.
What does this indicate? With merely three metrics, you can discern the advancements made in your DEI recruiting goals – and if the performance doesn’t meet the mark, it’s evident at which stage the targets falter.
It is important to note that the recommendations of our Ai are based solely on its analysis of candidate responses in the chat-based interview. Its suitability criteria is based, among other factors, on HEXACO personality modeling and accurate assessments of various job related competencies such as team work, critical thinking and communication skills.
Our data also keep biases in check at each stage of the recruitment process, depending on the role type. As you can see, for all three roles, this company’s hiring outcomes were within regulatory limits (as stipulated by the US Equal Employment Opportunity Commission (EEOC)) across the three stages of their funnel: Applications received, recommendations made, and the hiring decisions ultimately made by the hiring team. The final step, it is important to note, happens independently of our Ai: It is a human decision. Despite this, the outcome data is recorded, so that the company can compare its outcomes against inputs and recommendations to see if late-funnel biases are occurring.
Figure 2: Role-type-based gender bias. Mid line represents zero bias. Shaded regions signify the tolerance range. Right of line favors females, while left favors males.
The feedback from candidates is extremely positive: Company A’s strivings for fairness and equality in its processes has resulted in a candidate satisfaction score of 98.7% for females, and 98.1% for males. Better still, the interview dropout rate across the board is less than 10%.
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Ethnicity bias
Parallel to gender, our ethnicity analytics equip hiring managers to efficiently set and accurately monitor diversity smart goals examples for ethnic representation in recruitment. Company A, as depicted in Figure 2, is pioneering in this respect: Its BAME (Black, Asian, and Ethnic Minorities) recommendation rate stands at 46.5%, outpacing expectations, while its non-BAME recommendation rate is at 37.1%.
Our data has also helped Company A to increase its hiring commitments for First Nations people: The rate currently sits at 4.5%, from 4,000 candidates, above the national average of 1.8% (2018-19). This number is expected to increase over the coming year.
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Personality biases
The data we collect helps us, as well as our customers, understand the extent to which personality determines role suitability and general workplace success. It also helps us to eliminate long-standing biases that negatively impact certain candidates, despite the fact that said candidates may be highly suitable to the roles for which they are applying.
For example, people high in trait agreeableness (compassionate, polite, not likely to dissent or proffer controversial viewpoints) tend to underperform in the traditional face-to-face interviews. Hiring managers may assume, based on this, that they are unable to lead, or are not a ‘culture fit’. However, a face-value assessment of agreeableness is not a reliable predictor of candidate potential. Only scientific analysis of HEXACO traits can make this call with accuracy.
Take these two visualizations, showing how different personality traits affect the recommendations made by our Ai. Females (red dot) and males (blue dot) are slightly different in agreeableness, but there is virtually no difference in their conscientiousness, a strong predictor of job performance. As a result of being able to measure conscientiousness accurately, our system can effectively allow for higher levels of agreeableness – or cancel out the negative face-value judgements typically made in face-to-face interviews. Despite these personality differences, as shown in Figure 1, Sapia Ai recommendations for both male and female groups remain similar (~40%). This results in a fairer chance for all, and a wider pool of candidates. In this case, this is to the benefit of females.
Figure 3: Male (blue) and Female (red) personality trait differences
Bringing it all together
The world is changing, and we can no longer continue to take a “We’ll see what happens” approach to the ‘S’ in ESG. Many investors are pushing companies for better diversity and inclusion outcomes. At Sapia, our data show that fair, scientifically valid, and explainable Ai can produce better outcomes for peoples of all genders and ethnicities. The companies that have adopted our Ai approach are seeing strong improvement in their own DEI practices and results.
Over and above assisting our clients, our commitment to DEI is embodied in a guiding vision of our own: Our FAIR Framework. This embeds an approach that ensures our systems and processes are ethical and transparent. Many similar Ai systems operate in a ‘black box’, providing little knowledge about how their algorithms help make important decisions or create issues like amplifying biases. We are committed to a fairer world, free of bias – and, with every candidate interviewed, our data is bringing us closer.
Related
Blog
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: