Part of our job here in the workforce science team is to keep up to date with new research in Organisational Psychology. This might sound boring to some people – but we love it!
As massive nerds, we find nothing more exciting than seeing new progress in our field. This time, our knowledge-cravings took us all the way from Melbourne to Orlando, Florida, to this year’s SIOP conference.
The issue of Adverse Impact
An important issue within our field – and within the US in general – is adverse impact and hiring for diversity.
We are passionate about ensuring people are not discriminated against in selection methods, whether it is because of gender, age, ethnic background or sexual orientation.
(Actually, this is also one of the key values and driving forces behind why Paul, our CEO, founded Sapia.)
One key topic at this year’s conference was the combination of data science and behavioural science. Specifically, there were a lot of discussions around how these sciences can work together to minimise bias and discrimination in the hiring process.
The standard recruitment selection process
To give you some background as to why this is important, let’s explore what a standard selection process might look like today.
If you ever have applied for a job, it is likely you have gone through a process involving;
- your resume
- a cover letter
- a psychometric test (personality/intelligence)
- an interview
- reference checks
As mentioned, pretty standard. This is typically the different pieces of information that recruiters would use to assess your suitability for a role.
However, from an adverse impact perspective, this isn’t good enough.
The reason is that humans are biased (there are a plethora of studies out there proving this). And even if our biases (in most cases) are unconscious, we still base discriminatory decisions on them.
Does your name predict future performance?
A research study by The Ladders found that recruiters only spend about 6 seconds looking at a resume. Using gaze-tracking technology they identified that recruiters spend almost 80% of this time on only a few items:
- name
- current title/company
- previous title/company
- previous position start and end dates
- current position start and end dates
- education
To most people that would seem reasonable. Our previous professional and educational experience should be predictive of future performance, right?
If you agree, it might surprise you that past job experience only has a 0.13 validity when used to predict performance (and your name certainly has nothing to do with how you would perform).
So not only is the information recruiters look at not actually predictive of performance, but it also has the potential to adversely impact minorities.
An eye-opening example
In the 1970s, the Toronto Symphony Orchestra was composed of almost all white males. A few years later, they caught on to their diversity issue and decided to do something about it.
One initiative was to introduce ‘blind auditions’. Individuals would perform from behind a screen, making the assessors ‘blind’ to who was performing. This meant that the performance was in the center of the assessment, not the individual.
The result?
The proportion of women within the orchestra increased from 5% to 35%.
Individuals within racial minority groups are also discriminated against based on resumes.
Research found that applicants with ‘traditional’ english names received an interview for every 1/10 resumes sent out. This is in contrast to applicants with African-American names, who only got an interview for every 1/15 resumes.
As the resume is one of the most common determinators of whether an applicant progresses to the next stage – it is alarming that this method can adversely impact minority groups.
Luckily, some progress is definitely being made to combat this.
Different techniques, for example blind recruitment, are increasing in popularity. Some progressive businesses have leap-frogged and started using artificial intelligence (AI) driven algorithms as a first step in their assessment process.
Important to keep in mind with AI and Adverse Impact
When using AI, it is very important to understand that the data put into the algorithm is of great importance. AI is often touted as the solution to the biases inherent in our thinking, but if not executed properly, AI can also become biased.
This is because an AI algorithm is only ever as bias-free as the data we used to build it.
It can be difficult to make sure AI is increasing diversity, and at the same time maintaining its predictive power. The predictive power is basically how good a model is at predicting good performance – and weeding out those who wouldn’t do so well.
To ensure best chance of success it is crucial that the data we put into AI recruitment tools is bias free.
One way is to control what you put into your AI models. Big Data can for example be dangerous, as it looks at adding all possible data sources of information to predict performance.
This could mean that the AI model learns that ethnic background is a predictor for success, which we clearly want to avoid.
To combat this issue at Sapia, we make the following decisions:
Targeted variables:
- we only choose variables that minority groups do not answer differently to other groups
(if we did the model could learn to discriminate against these groups if the variable was considered predictive)
Test our predictors:
- Are they in fact adversely impacting anyone?
- Conduct adverse impact studies
When considering a new assessment tool, you should always ask your test provider the following;
How do you ensure the assessment isn’t biased against any gender, age or racial category, whilst remaining highly predictive of performance?
If they can’t give you a satisfying answer, it is definitely worthwhile considering another vendor.
Liked what you read? For further reading on how we minimise bias in our algorithms, head here.
Related
Blog
Humanising hiring: the largest study of AI candidate experience ever
This is the state of hiring in 2025. Too often, candidates are ghosted, ignored, and reduced to a CV. Recruiters are forced to make decisions in data poverty, with scraps of information like grades, job titles, or where someone has worked before. Privilege gets rewarded; potential gets overlooked.
For the first time, we now have evidence that AI, when designed responsibly, brings humanity back to hiring.
The largest research study of its kind
Sapia.ai has released the Humanising Hiring report. The largest analysis ever conducted into candidate experience with AI interviews. The study draws on more than 1 million interviews and 11 million words of candidate feedback across 30+ countries.
Unlike surveys or anecdotal reviews, this research is grounded in what candidates themselves chose to share at one of the most stressful moments of their lives: applying for a job.
The findings are bold and unprecedented
- 9.05/10 average candidate satisfaction across all groups and industries
- 81.8% of candidates left written feedback — engagement at this scale has never been seen before in hiring research
- 8 in 10 candidates would recommend an employer just because of the interview
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30% more women apply when told AI will assess them, resulting in a 36% closure of the gender gap
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98% hiring equity for people with disabilities through a blind, untimed, mobile-first interview design
Candidate voices
Here’s what candidates themselves revealed:
“None of the other companies I’ve applied to do this sort of thing. It’s so unique and wonderful to give this sort of insight to people… whether we get the job or not, we can take away something very valuable out of the process.”
“That felt so personal, as if the person genuinely took the time to read my answers and send me a summary of myself… that was pretty amazing.”
Expert validation
“This study stands out as one of the most comprehensive examinations of candidate experience to date. Analysing over a million interviews and 11 million words of candidate feedback, the findings make clear that responsibly designed AI has the potential to fundamentally improve hiring — not just by increasing speed, but by advancing fairness, enhancing the human aspect, and leading to stronger job matches.”
— Kathi Enderes, SVP Research & Global Industry Analyst, The Josh Bersin Company
Proof that AI can be human
The research challenges the idea that AI dehumanises the hiring process. In fact, it proves the opposite: when thoughtfully designed, AI can restore dignity to candidates by giving them a real interview from the very first interaction, giving them space to share their story, and giving them timely feedback.
With Sapia.ai’s Chat Interview:
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Every candidate gets the same structured, role-relevant questions.
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Interviews are untimed, so candidates can answer at their own pace.
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Bias is monitored continuously under our FAIR™ framework.
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Every candidate receives personalised feedback.
This isn’t automation for the sake of speed. It’s intelligence that puts people first, and it works. Leading global brands, including Qantas, Joe & the Juice, BT Group, Holland & Barrett, and Woolworths, have all transformed their hiring outcomes while enhancing the candidate experience.
Why it matters now
Applicant volumes are exploding. Boards are demanding ROI on people decisions. And candidates expect fairness and agency. Sticking with the status quo — ghosting, inconsistent interviews, CV screening — comes at a real cost in brand equity, lost talent, and wasted time.
It’s time to move from data poverty to data richness, from broken processes to brilliant hiring.
Download the report
This is the first time candidate feedback on AI interviews has been analysed at such scale. The insights are clear: hiring can be brilliant.
👉 Download the Humanising Hiring report now to see the full findings.
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.