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Is it time to start trusting the machine?

Machine learning outcomes are testable and corrective measures remain consistent, unlike in humans.

“People are not your most important asset. The right people are.” – Jim Collins, author and lecturer on company sustainability and growth.

“Are the right people in the right roles?” [This is] the single most important factor for leadership success and for organisational success.” – Gail Kelly, former CEO of Westpac

How many research papers do we need to read or edicts from top-class CEOs before we get the message that in every organisation, it all comes down to the people?

Adam Bryant who pens the terrific weekly column, The Corner Office, for the NYT has interviewed a diverse pool of leaders, and a common theme from 99 % of his interviews with CEOs is that success correlates with hiring the best team.

My former boss Tracey Fellows, CEO of the REA Group, was also fond of saying that it is ‘people’ that keeps her up at night more than any other business challenge.

Most hiring in most organisations relies 100 % on people to make those most important decisions. Yet we do so with little objective data. Instead, we have layers upon layers of bias! And to give you an idea of how many there are, here is a whooping full Wikipedia list of cognitive biases for you to check out. This article lays out in great detail a plethora of cloudy, smeary and hazy biases I didn’t know could exist.

It concludes that they are mostly unalterable and fixed, regardless of how much unconscious bias training you attend in your lifetime.

There is no scalable, efficient and reliable way to train us out of our biases. Our biases are so embedded and invisible; mostly, we just can’t ‘check ourselves’ at the moment to manage them.

So, how is that diversity hiring program going?

Read: Why a Lack of Diversity is Costing Your Business

In some functions/ departments, your “Hiring for Diversity” may be going very well. However, diversity training and hiring isn’t repeatable, where humans are involved. And, if humans could be trained out of their biases, we may get more diversity in our new hires. But then, do we know that we are getting the ‘better’ hire from the applicant pool? How CAN you tell if you have no method of reliably testing for what matters for success?

You might say we rely on CVs to give us that ‘insight’ but did you know CVs are usually crafted, designed, worded and reworded to ‘best-light’ the applicant. Ever appointed an Excel whizz, who on hire doesn’t know a pivot from a concatenate? Or even worse, who cannot apply logic, reasoning and critical thought?

We have all done this – apply crude (biased) filters to screen applications:

  • Blue-chip companies on their CV – tick!
  • Stayed in their role for two years on average – tick!
  • Promoted at least once inside of a (good) organisation – tick!
  • Good school – tick!
  • Impressive referees – tick tick tick!

Because biases appear to be so hardwired and inalterable, it is more straightforward to remove bias from algorithms than from people.

This gives AI the potential to create a future where important insights underpinning decisions such as hiring, are made more fairly.

The machine can be trained to help you make repeatable and stable decisions.

Read: Why Machines make better decisions than humans (oh and why I hate Simon Sinek)

Algorithmic bias is not the elephant in the room. Some argue that algorithms themselves have bias. The reality is that machine learning, by its very definition, is aiming to find patterns in large volumes of data, mostly latent, to support decisive actions. Removing bias is driven by what bits of training data you use to feed the machine.

You can ensure there is no (or limited) bias in the machine learning and it is all about two things:

  1. What data is being used to build the model?
  2. What are you doing to that data to build the model?

If you build models from the profile of your talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self-reinforcing hiring.

If you are using data which looks at age, gender, ethnicity and all those visible markers of bias, then, sure enough, you will amplify that bias in your machine learning. Relying on internal performance data to make people decisions, that is like layering bias-upon-bias. Similar to building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.

So instead of lumping all AI and ML into one big bucket of ‘bias’, look beneath the surface to understand what’s going into the machine as that is where amplification risks loom large.

To ensure you are using machine learning wisely, only use objective data which has no biodata (that means a big NO to CV and social media scraping). Test rigorously and adjust to learn continuously. And, be certain to use multiple machine learning models to continuously triangulate the model versus relying on one version of the truth.

Machines are better at learning this stuff.

Unlike trying to solve human bias, machine learning is repeatable, stable, consistent and most importantly, testable. The value to the organisation is of course, immense.

  • Every applicant gets a fair go at the role;
  • Every applicant is assessed;
  • Hire the person who will succeed vs someone your gut tells you will succeed;
  • Use fewer resources to hire;
  • Reduce the cost of hire.

Now that is ticking all the right boxes. It makes the possibility of objective and valid decisions available at scale, a probability.

Machine learning outcomes are testable and corrective measures remain consistent, unlike in humans.

The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.

Soon (maybe already) you will be putting yours, and your loved ones live in the hands of algorithms when you ride in that self-driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data, even in hiring.

To keep up to date on all things “Hiring with Ai” and Machine Learning subscribe to our blog!

You can try out Sapia’s Chat Interview right now – HERE. Else leave us your details to get a personalised demo


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Sapia.ai certified to ISO/IEC 42001: Setting the global standard for Responsible AI in hiring

Ethical AI doesn’t happen by accident. It happens through transparency, rigorous science, and strong governance.

We are proud to share that Sapia.ai has achieved ISO/IEC 42001:2023, the world’s first international standard for AI management systems. This certification is independent proof that every part of our AI, from design to deployment, follows a framework of integrity, accountability, and fairness.

Governance before growth

While many providers rush AI features to market, we have taken a different path by putting governance first. Since 2018, our platform has been built on science and ethics.

In 2021, we introduced the FAIR™ Framework (Fair AI for Recruitment), setting a global benchmark for what responsible AI in hiring should look like: explainable, inclusive, and continuously tested for bias. Achieving ISO 42001 builds on that foundation, formalising years of responsible practice.

“Responsible AI isn’t new for us, it’s our foundation,” says Barb Hyman, CEO & Founder. “Our customers trust us with decisions that affect people’s lives. ISO 42001 provides further proof that our systems are built for accountability and transparency.”

Built on science and secure by design

Sapia.ai is the first AI interview company to achieve ISO 42001 certification, reflecting our approach to responsible data use, model training, and validation.

Our models are trained on behavioural data from more than eight million structured interviews and three billion words of human responses, measuring genuine skills and competencies rather than using inferred or scraped data. This foundation ensures that our AI is grounded in evidence and fairness.

We also maintain a full suite of enterprise-grade security and compliance credentials, including:

  • ISO 27001 certification for information security management

  • SOC 2 Type II attestation

  • GDPR and UK DPA 2018 compliance

  • AWS Bedrock data hosting, which ensures zero data sharing or retention with LLM providers

  • Privacy by Design and regular third-party security audits

Each certification supports the same goal: giving customers confidence that innovation is backed by integrity.

What it means for HR leaders

With the EU AI Act about to reshape how enterprises govern AI, independent validation has never mattered more. ISO 42001 demonstrates that Sapia.ai already meets these standards: ethical, compliant, and explainable by design.

Our mission is to prove that AI can be both powerful and principled, helping organisations hire faster and fairer while preserving the dignity of every candidate.

Responsible AI is both good governance, good business, and it’s how brilliant hiring gets done.

FAQ: Responsible AI and ISO 42001 Certification

1. What is ISO/IEC 42001:2023?
ISO/IEC 42001:2023 is the world’s first international standard for AI management systems. It sets out how organisations should design, implement, and monitor AI responsibly. The framework ensures transparency, fairness, and accountability across all AI operations.

2. Why is ISO 42001 important for HR and recruitment?
For HR and TA leaders, ISO 42001 certification provides assurance that AI systems used in hiring meet global standards for governance and compliance. It reduces risk under new regulations such as the EU AI Act and demonstrates a commitment to fairness, transparency, and data protection.

3. How does Sapia.ai ensure ethical use of AI in hiring?
Sapia.ai’s platform is built on the FAIR™ Framework (Fair AI for Recruitment), a science-backed model for designing, testing, and monitoring AI that is fair, explainable, and inclusive. All models are validated, bias-tested, and audited regularly to ensure consistent fairness across candidate groups.

4. What other certifications does Sapia.ai hold?
In addition to ISO 42001, Sapia.ai holds ISO 27001 certification for information security management, SOC 2 Type II attestation, and full GDPR and UK DPA 2018 compliance. The platform is hosted on AWS Bedrock, ensuring that data is never shared or retained by LLM providers.

5. How does Sapia.ai differ from other AI hiring tools?
Sapia.ai is an AI-native platform built on behavioural science and more than eight million structured interviews. Unlike generic AI tools, Sapia.ai measures real skills and competencies using structured, conversational assessments that are inclusive and explainable by design.

6. What does this mean for candidates?
Every candidate who completes a Sapia.ai Chat Interview receives feedback and insights, ensuring a transparent and respectful experience. This approach restores fairness and dignity to hiring, aligning with our broader mission to humanise recruitment.

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Why generic AI belongs nowhere near your hiring process

If you’ve experimented with tools like ChatGPT, Claude, or Gemini, you’ve probably experienced this: ask the same question twice and you’ll often get two different answers.

This is by design. Gen AI models are probabilistic. They generate answers by predicting the “next most likely word,” with an intentional dose of randomness (temperature, sampling) to make them feel more “human.”

When you use that design principle in recruitment, you’re playing with fire.

Variability = Risk

Imagine using generic AI for:

  • Screening CVs for “culture fit”
  • Generating interview questions
  • Evaluating candidate responses
  • Writing job adverts

If the same input produces different outputs depending on the day, you have a trust problem:

  • Inconsistent screening = discrimination claims waiting to happen
  • Variable interview guides = unfair candidate experiences
  • Drifting evaluation criteria = missed talent
  • Mixed messaging = damaged employer brand

Hiring decisions are high-stakes. Candidates deserve certainty and fairness. Employers need defensibility. Probabilistic creativity is great for drafting emails or brainstorming headlines. It does not belong where the output affects someone’s career.

Thin wrappers, big risks

What we’re now seeing in the market is a proliferation of “thin wrappers”. Hiring tools that are built quickly on top of open-source AI models. The logic is simple: take a model like Qwen, Mistral, or LLaMA, put a UI around it, and call it a recruitment solution. 

The problem? These wrappers inherit all the instability of their foundation models. And worse, they add risk x10:

  • Data governance: Who owns the candidate data once it touches that model? How is it being stored, trained, and reused?
  • Bias & fairness: If outputs vary day-to-day, bias testing becomes impossible. What are you even testing against?
  • Regulatory exposure: Under the EU AI Act, voice or biometric data misused in training is a compliance nightmare. Under GDPR Article 9, it constitutes a breach of sensitive data handling.
  • Enterprise readiness: A startup wrapping an open model cannot match the scale, validation, or auditability that global enterprises require.

This is the hidden risk of generic AI in the hiring process. On the surface, it looks sleek, fast, and innovative. Underneath, it’s a house built on sand.

The alternative: A specialised AI system built for measurement, not inference

At Sapia.ai, we’ve taken a very different path. We’ve built a for-purpose AI system designed specifically for hiring, utilising methods published in peer-reviewed journals.  

Over the last eight years, we’ve conducted more than 8 million structured, conversational interviews across 50 countries and 20 languages. Every response is scored against validated competencies, ensuring that our assessments are: 

  • Structured, not random: Every candidate gets the same questions. Every answer is scored against the same criteria.
  • Transparent: You can see how scoring works. Every candidate gets personalised feedback.
  • Governed: Our FAIR™ framework, ISO 42001 alignment, and regular bias testing mean enterprises can adopt AI responsibly.
  • Loved by candidates: 91% completion rates, 9/10 experience ratings.

This isn’t a thin wrapper. It’s an AI system designed from the ground up for hiring, with fairness, science, and trust at its core.

Why should HR leaders care? 

The convergence of Talent Acquisition, Talent Management, and Reskilling means the pressure on HR leaders has never been higher. Everyone wants internal mobility, but the default playbook (job boards, CV self-mapping) rarely delivers.

If the tools you adopt today are built on randomness and inference, you’re not just risking a poor candidate experience. You’re risking lawsuits, compliance failures, and reputational damage.

If instead, you invest in measurement, structure, and science, you create a workforce data asset that compounds in value, unlocking hiring intelligence, mobility pathways, and skills development at scale.

Variability is a liability

Generative AI has transformed how we create at pace and at scale. But let’s not confuse creativity with science. Recruitment isn’t about “good enough, most of the time.” It’s about fairness, rigour, and trust.

For those who want to understand more, check out our ebook Understanding Responsible AI in Recruitment.

Frequently Asked Questions (FAQ)

  1. Why is generic AI risky to use in hiring?
     Generic AI models like ChatGPT or Gemini are probabilistic. They generate different answers to the same input. In hiring, that variability creates risk: inconsistent candidate screening, unfair interview questions, and drifting evaluation criteria. For employers, this opens the door to discrimination claims, compliance failures, and reputational damage.
  2. What are “thin wrapper” recruitment tools, and why are they dangerous?
     Many new hiring tools are “thin wrappers” built on top of open-source models like LLaMA or Mistral. They inherit instability from their foundational models and introduce additional enterprise risks, including unclear data ownership, a lack of fairness testing, and poor regulatory compliance. In contrast, purpose-built systems like Sapia.ai are validated, explainable, and designed for defensibility.
  3. How does Sapia.ai ensure fairness in AI hiring?
     Sapia.ai adheres to the FAIR™ Framework, a global standard that ensures AI-powered hiring is unbiased, explainable, valid, and inclusive. Every candidate receives the same structured questions, and responses are scored against validated criteria. Models undergo continuous bias testing. Independent research shows Sapia.ai reduces the gender gap in hiring by up to 36%.
  4. Does purpose-built AI really help increase diversity in hiring?
     Yes. A Monash/Gothenburg University study using Sapia.ai found that when candidates knew AI would assess their application, 30% more women applied, without reducing quality or volume. When paired with AI scoring, evaluators selected men and women equally, closing the gender gap by 36%.
  5. How does AI improve the candidate experience?
     Unlike CV screening or psychometric tests, Sapia.ai’s Chat Interview is untimed, text-based, and mobile-first. Candidates can respond in their own time and receive personalised insights, not just a rejection. Across 8M+ interviews, the average candidate satisfaction score is 9.05/10, with over 80% voluntarily leaving feedback.
  6. Is Sapia.ai inclusive for people with disabilities and neurodiverse candidates?
    Yes. Traditional hiring (video interviews, timed assessments) can disadvantage people with disabilities. Sapia.ai’s chat format removes those barriers: it’s untimed, compatible with screen readers, and designed for psychological safety. Research shows candidates with disabilities progress through the funnel at the same rate as others, ensuring hiring equity of up to 98%.
  7. How does Sapia.ai protect candidate data?
     All data is processed securely through AWS Bedrock, ensuring nothing is shared with or retained by model providers. Sapia.ai does not use demographic data (e.g., gender, age, race) for scoring, and complies with GDPR, ISO 27001, and ISO 42001 standards. Learn more about our security and compliance in our Trust Centre.
  8. How is Sapia.ai different from generic generative AI tools?
     Unlike generic GenAI, which is built for creativity, Sapia.ai is built for measurement. Every candidate is assessed against the same validated competencies, creating structure, transparency, and defensibility. This makes it enterprise-ready, compliance-aligned, and trusted by brands like Woolworths, Qantas, BT and Holland & Barrett
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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

  • 30% more women apply when told AI will assess them, resulting in a 36% closure of the gender gap

  • 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:

  • Every candidate gets the same structured, role-relevant questions.

  • Interviews are untimed, so candidates can answer at their own pace.

  • Bias is monitored continuously under our FAIR™ framework.

  • 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.


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