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
What’s More Ethical: Measuring Skills or Guessing Them?
Barb Hyman, CEO & Founder, Sapia.ai
Why skills data matters for HR and CHROs
Every CHRO I speak to wants clarity on skills:
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What skills do we have today?
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What skills do we need tomorrow?
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How do we close the gap?
The skills-based organisation has become HR’s holy grail. But not all skills data is created equal. The way you capture it has ethical consequences.
Two very different approaches to skills analysis
1. Skills inference from digital traces
Some vendors mine employees’ “digital exhaust” by scanning emails, CRM activity, project tickets and Slack messages to guess what skills someone has.
It is broad and fast, but fairness is a real concern.
2. Skills measurement through structured conversations
The alternative is to measure skills directly. Structured, science-backed conversations reveal behaviours, competencies and potential. This data is transparent, explainable and given with consent.
It takes longer to build, but it is grounded in reality.
The risks of skills inference HR leaders must confront
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Surveillance and trust: Do your people know their digital trails are being mined? What happens when they find out?
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Bias: Who writes more Slack updates, introverts or extroverts? Who logs more Jira tickets, engineers or managers? Behaviour is not the same as skills.
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Explainability: If an algorithm says, “You are good at negotiation” because you sent lots of emails, how can you validate that?
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Agency: If a system builds a skills profile without consent, do employees have control over their own career data?
A more human approach: skills measurement
Skills define careers. They shape mobility, pay and opportunity. That makes how you measure them an ethical choice as well as a technical one.
At Sapia.ai, we have shown that structured, untimed, conversational AI interviews restore dignity in hiring and skills measurement. Over 8 million interviews across 50+ languages prove that candidates prefer transparent and fair processes that let them share who they are, in their own words.
Skills measurement is about trust, fairness and people’s futures.
Questions every HR and CHRO should ask
When evaluating skills solutions, ask:
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Is this system measuring real skills, or only inferring them from proxies?
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Would I be comfortable if employees knew exactly how their skills profile was created?
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Does this process give people agency over their data, or take it away?
The real test of ethics in the skills-based organisation
The choice is between skills data that is guessed from digital traces and skills data that is earned through evidence, reflection and dialogue.
If you want trust in your people decisions, choose measurement over inference.
To see how candidates really feel about ethical skills measurement, check out our latest research report: Humanising Hiring, the largest scale analysis of candidate experience of AI interviews – ever.
FAQs
What is the most ethical way to measure skills?
The most ethical method is to use structured, science-backed conversations that assess behaviours, competencies and potential with consent and transparency.
Why is skills inference problematic?
Skills inference relies on digital traces such as emails or Slack activity, which can introduce bias, raise privacy concerns and reduce employee trust.
How does ethical AI help with skills measurement?
Ethical AI, such as structured conversational interviews, ensures fairness by using consistent data, removing demographic bias and giving every candidate or employee a voice.
What should HR leaders look for in a skills platform?
Look for transparency, explainability, inclusivity and evidence that the platform measures skills directly rather than guessing from digital behaviour.
How does Sapia.ai support ethical skills measurement?
Sapia.ai uses structured, untimed chat interviews in over 50 languages. Every candidate receives
Blog
Mirrored diversity: why retail teams should look like their customers
Walk into any store this festive season and you’ll see it instantly. The lights, the displays, the products are all crafted to draw people in. Retailers spend millions on campaigns to bring customers through the door.
But the real moment of truth isn’t the emotional TV ad, or the shimmering window display. It’s the human standing behind the counter. That person is the brand.
The missing link in retail hiring
Most retailers know this, yet their hiring processes tell a different story. Candidates are often screened by rigid CV reviews or psychometric tests that force them into boxes. Neurodiverse candidates, career changers, and people from different cultural or educational backgrounds are often the ones who fall through the cracks.
And yet, these are the very people who may best understand your customers. If your store colleagues don’t reflect the diversity of the communities you serve, you create distance where there should be connection. You lose loyalty. You lose growth.
We call this gap the diversity mirror.
What mirrored diversity looks like
When retailers achieve mirrored diversity, their teams look like their customers:
- A grocery store team that reflects the cultural mix of its neighbourhood.
- A fashion store with colleagues who understand both style and accessibility.
- A beauty retailer whose teams reflect every skin tone, gender, and background that walks through the door.
Customers buy where they feel seen – making this a commercial imperative.
How to recruit seasonal employees with mirrored diversity
The challenge for HR leaders is that most hiring systems are biased by design. CVs privilege pedigree over potential. Multiple-choice tests reduce people to stereotypes. And rushed festive hiring campaigns only compound the problem.
That’s where Sapia.ai changes the equation: Every candidate is interviewed automatically, fairly, and in their own words.
- Bias is measured and monitored using Sapia.ai’s FAIR™ framework.
- Outcomes are validated at scale: 7+ million candidates, 52 countries, average candidate satisfaction 9.2/10.
- Diversity can be measured: with the Diversity Dashboard, you can track DEI capture rates, candidate engagement, and diversity hiring outcomes across every stage of the funnel.
With the right HR hiring tools, mirrored diversity becomes a data point you can track, prove, and deliver on. It’s no longer just a slogan.
Retail recruiting strategies in action: the David Jones example
David Jones, Australia’s premium department store, put this into practice:
- 40,000 festive applicants screened automatically
- 80% of final hires recommended by Sapia.ai
- Recruiters freed up 4,000 hours in screening time
- Candidate experience rated 9.1/10
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
Recruiting ideas for retail leaders this festive season
As you prepare for festive hiring in the UK and Europe, ask yourself:
- How much will you spend on marketing this Christmas?
- And how much will you invest in ensuring the colleagues who deliver that brand promise reflect the people you want in your stores?
Because when your colleagues mirror your customers, you achieve growth, and by design, you’ll achieve inclusion.
See how Sapia.ai can help you achieve mirrored diversity this festive season. Book a demo with our team here.
FAQs on retail recruitment and mirrored diversity
What is mirrored diversity in retail?
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Why is diversity important in seasonal retail hiring?
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
How can retailers improve their hiring strategies?
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
Blog
The Diversity Dashboard: Proving your DEI strategy is working
Why measuring diversity matters
Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.
Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.
Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.
What is the Diversity Dashboard?
The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.
Key features include:
- Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
- Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
- Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
- Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
- Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.
From insight to action
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
- See if your EASL candidates are applying in high numbers but not progressing to live interview.
- Spot if candidates with a disability report high satisfaction but have lower offer rates.
- Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.
Built on science, backed by trust
Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.
Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.
Book a demo to see the Diversity Dashboard in action.