COVID has taught us that on reflection the focus on individual action with a community benefit as a goal is really a focus that leads to the greater good. In our home state of Victoria, Australia now 7 straight days with ZERO new cases. It has been an effort founded on facts and science over misinformation. In Victoria, many sacrificed a lot for their well-being for ALL. If anything, there is now proof, thanks to Victorians, that when we see facts, listen to science and let data show you how to lead that change, you can make it happen.
AI, especially predictive machine learning models, are an outcome of a scientific process, it’s no different to any other scientific theory, where a hypothesis is being tested using data.
The beauty of the scientific method is that every scientific theory needs to be falsifiable, a condition first brought to light by the philosopher of science Karl Popper. In other words, a theory has to have the capacity to be contradicted with evidence.
There are three decisions that are made by a human in building that scientific experiment.
One can argue 2 and 3 are the same as if the methodology is not sound the data collection wouldn’t be either. That’s why there is so much challenge and curiosity as there should be about the data that goes into an algorithm.
Think of an analogy in a different field of science: the science of climate change.
A scientist comes up with a hypothesis that certain factors drive an increase in objective measures of climate warming, eg CO2 emissions, cars on the road, etc. That’s a hypothesis and then she tests it using statistical analysis to prove or disprove that her hypothesis holds beyond random chance.
The best way to make sure you are following a sound scientific approach is to share your findings with the broader scientific community. In other words, publish in peer-reviewed mediums such as journals or conferences so that you are open to scrutiny and arguments against your findings.
Or to put it another way, be open for your hypothesis to be falsified.
In AI especially, it is also important to keep testing whether your hypothesis holds over time as new data may show patterns that lead to disproving your initial hypothesis. This can be due to limitations in your initial dataset or assumptions made that are no longer valid. For example, assuming the only information in a resume related to gender are name and explicit mention of gender or a certain predictive pattern such as detecting facial expressions are consistent across race or gender groups. Both of these have been proven wrong*.
The only way to improve our ability to predict, be it climate change or employee performance, is to start applying the scientific method and be open to adjusting your models to better explain new evidence.
Therefore the idea that a human can encode their own biases in the AI — well it’s just not true if the right science is followed.
* Amazon scraps secret AI recruiting tool that showed bias against women (https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G)
* Researchers find evidence of bias in facial expression data sets (https://venturebeat.com/2020/07/24/researchers-find-evidence-of-bias-in-facial-expression-data-sets/)
You can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo
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.