An HR Algorithms Can Tell How Often You Will Change Jobs

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

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.

Barb Hyman, CEO & Founder, Sapia.ai

Why skills data matters for HR and CHROs

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.

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

• 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?

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:

• 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 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