This seems obvious but yet even today this is the key data source used in screening and hiring. For grad recruitment, your degree, your university and your uni results are key filters used in screening.
It’s already been four years since Ernst & Young removed university degree classification as an entry criterion as there is ‘no evidence’ it equals success. Students are savvy and they know how competitive it is to secure a top graduate job. In the UK, the Higher Education Degree Datacheck (Hedd) surveys students and graduates about degree fraud. The annual results are pretty consistent – about a third of people embellish or exaggerate their academic qualifications when applying for jobs. Read more here >
We analysed ~13,000 CVs, received over a 5 year period, all for similar roles for a large sales-led organisation. From this data set, 2660 were hired and around 9600 rejected. We wanted to test how meaningful the CV is as a data source for hiring decisions.
Look at these two word-clouds. One represents the words extracted from the CVs of those who were and the other from those who were rejected. Which would you pick?
A word cloud depicts the relative frequency of words appearing in the set of resumes by the size of the words in the word cloud, i.e. words in larger font size appears more than the ones in smaller font size. Given that the two word clouds show no significant differences in the words in larger or smaller font sizes means that the two groups are indistinguishable based on the words used within CV’s.
P.S If you had picked Group 2 you would have been right.
Josh Bersin, the premier topic expert in our space, articulates how hard it is to predict performance through traditional testing in this way .
“Managers and HR professionals use billions of dollars of assessment, tests, simulations, and games to hire people – yet many tell me they still get 30-40% of their candidates wrong.”
And now the definitive publication for all things HR, leadership etc. the Harvard Business Review, has shared research that prior experience is also a poor predictor of performance. Read more >
Whether their background is similar to yours or the person in your team who is a star? Whether they have played a competitive sport at a senior level (because that’s a good indicator of drive and resilience)? Or maybe whether they are a different ethnicity, gender, educational background to the rest of your team because, you know … diversity is meant to be good for business!
The list of performance ‘signals’ are as many as the number of people (interviewers) you have interviewing new hires. It’s a deeply personal decision like who you choose as a partner and we all feel like we know what to look for. But we don’t.
And no amount of interview or bias training or even interview experience is ever going to make us better at these decisions.
But experience does matter, but it’s a different type of experience. It’s the experience that comes from doing something 10 x, 10,000 times, a million times, with feedback on what worked, what didn’t, under what context etc. And of course, if one could remember all that.
Think of a different context- the grading of an exam. If you ask your teenager or university-aged son/daughter what would make them trust an exam result, they would likely say
1. Consistency
2. Anonymity
3. Data-driven, i.e some kind of formula for assessment, that assures consistency and fairness.
4. The experience of the assessor.
The fact is … just as no human driver will ever match the learning capability and velocity of a self-driving Tesla car, no assessor will ever be as good as a machine that’s done it a million times. The same applies for AI in recruitment.
No human recruiter will ever match the power, smarts and anonymity presented by a machine learning assessment algorithm.
We would love to see you join the conversation on LinkedIn!
Suggested Reading:
https://sapia.ai/blog/everybody-lies/
Barb Hyman, CEO & Founder, Sapia.ai
Every CHRO I speak to wants clarity on skills:
What skills do we have today?
What skills do we need tomorrow?
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.
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.
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.
Surveillance and trust: Do your people know their digital trails are being mined? What happens when they find out?
Bias: Who writes more Slack updates, introverts or extroverts? Who logs more Jira tickets, engineers or managers? Behaviour is not the same as skills.
Explainability: If an algorithm says, “You are good at negotiation” because you sent lots of emails, how can you validate that?
Agency: If a system builds a skills profile without consent, do employees have control over their own career data?
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.
When evaluating skills solutions, ask:
Is this system measuring real skills, or only inferring them from proxies?
Would I be comfortable if employees knew exactly how their skills profile was created?
Does this process give people agency over their data, or take it away?
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.
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
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.
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.
When retailers achieve mirrored diversity, their teams look like their customers:
Customers buy where they feel seen – making this a commercial imperative.
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.
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.
David Jones, Australia’s premium department store, put this into practice:
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
As you prepare for festive hiring in the UK and Europe, ask yourself:
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.
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
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.
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:
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
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.
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.