Hiring Under PDPL? You Can’t Afford an AI That Hallucinates

1. The Human Cost of Hallucinated Hiring

AI Hallucinations are very similar to Human hallucinations, where the AI hallucinates responses and fills in the gaps when it does not have accurate information. AI then responds on the basis of assumptions, not real facts. 

Think about this: a candidate mentions “Excel” on their CV. The AI decides that the candidate is a data analytics expert, or someone answers a few teamwork questions in an interview and the AI writes them up as a “natural leader.” It sounds smart. But none of it’s real. These results are ruled out by some assumptions made by AI. 

And that’s the problem: when your recruitment software or applicant tracking system starts making things up, the whole hiring process becomes shaky. You could end up rejecting someone who was actually perfect for the role, or worse, hiring someone who isn’t a fit at all.

This doesn’t just slow things down. It creates extra work for hiring managers and adds pressure to fix decisions that should’ve been right the first time. In places like Saudi Arabia, where companies are working towards Saudization goals and following strict rules under PDPL, one wrong hiring decision based on made-up data can lead to compliance issues, too. So yeah, AI hallucinations aren’t just a glitch. They’re something companies really need to take seriously.

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2. Why AI Hallucinates in Recruitment

AI is trained on data, and the more it is trained, the better the responses you get but in scenarios where the data itself is not of good quality, the response will never come out good and hence, AI may seem like a genius, but it is only the training which makes it better.

The recruitment software and applicant tracking system, we are talking about, are all trained using CVs and profiles gathered from various sources such as Job portals ( LinkedIn, Monster & etc), the old resumes stored in the ATS, and other sources could be the data gathered from the foreign market. So when AI is used for talent acquisition in a country like KSA, the AI might have a hard time getting the right context. 

That’s when hallucinations happen. The AI sees a keyword and jumps to conclusions. It doesn’t fully understand things like tone, culture, or the difference between what someone says and what they mean. For example, AI might notice a pause in a video interview and decide the candidate lacks confidence, which isn’t just wrong, it’s totally unfair. These systems often don’t show their backend process either. They just give you a score or summary without explaining how they got there.

At Recruitment Smart, we try to fix that. Our tools, like SniperAI, are built with verified data and tested to avoid this kind of guesswork. They don’t just match based on keywords; our products are built on the methodology of skills ontology, which helps in matching the skill set with the job requirements, which is a process that just doesn't rely on keyword matching techniques. Because let’s be honest, we are filling an open position, not doing some SEO for our website, we need more than just results, but the context through which the results have been derived. They look at patterns that make sense. And if they’re not sure? They say so. Because it’s better to pause and check than to make something up.

2.1 Skills Ontology: But How Ours is Different from the Rest? 

Let’s be real, most AI hiring tools try to sound smarter than they actually are. Behind the scenes, they rely on something called a skills ontology. It’s basically a giant database that maps how different skills relate to each other. Like: if someone knows Excel, maybe they also know data analytics. If they worked in HR, maybe they’re good at operations too.

But here’s the problem: most of these maps aren’t built using real hiring data. They’re pulled from old job boards, internet CVs, and vague assumptions. So the AI starts connecting dots that don’t actually go together.

That’s how you get:

• “Excel” = “Data Scientist”
  
• “Team project” = “Leadership potential”
  
• “Fluent speaker” = “Culture fit”
  

It may sound super intelligent, but this is all a guesswork by the AI, and it gets worse when the same AI is used by you to analyse the candidate interviews. Some tools use language models to judge tonality & energy levels. But if they weren’t trained on local accents or cultural norms, especially in places like Saudi Arabia. There are high chances they misread the cues and interpret the wrong results. At Recruitment Smart, we said a big NO to any guesswork. We built our own “Skills Ontology” from scratch, based on verified hiring data, not scraped junk and trained it on local jargon, cultural norms, etc. So our system only connects skills that go together, based on real roles, in real companies.

And if it’s not sure? It straight-up denies. No fake summary generation & no made-up confidence. We believe that instead of keeping the client or the candidate in grey zones, which creates even more complications and confusion, give them solutions which are more reliable and trustworthy. Because if you can not see what is going on in the background, you are putting your brand image on fire and losing some really good personnel, just because the AI said so!

Read More- Building a Future-Ready Workforce: A Comprehensive Guide to Skills Ontology Implementation

2.2 Made In-House: AI that is built on trust & the right algorithms

This is a topic which we love to talk about, as many other vendors avoid this conversation. A lot of the “AI” in recruitment tools today isn’t actually theirs. Behind the scenes, many platforms are just using OpenAI’s models. They wrap a pretty interface around ChatGPT or similar tech, add a few workflows, and call it a product. The vendors you are relying on with your money and future decisions, in reality, do not even have control over the model, and hence, they cannot even explain the logic behind the results of the AI.

We don’t do that. At Recruitment Smart, our AI,  including SniperAI, VScreen, and JeevesAI, is fully developed, trained, and maintained in-house. It's not a rented solution that we are talking about; we provide real solutions built on fully controlled AI, where we decide how the AI should respond and hence we can provide a complete logic behind the results. 

Here’s why that matters:

• No data leakage: Your candidate details stay private and local (especially critical for PDPL compliance in Saudi Arabia).
  
  
• Full explainability: The model is In-house, hence we can explain each step and process without losing transparency of results.
  
  
• Bias control: We do not depend on any third party's priority list; we build our own, and we rely on that.
  

Owning the AI means we take full responsibility & accountability for what it does. And that’s the only way we’re comfortable putting it in front of your hiring decisions

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3. Hallucination + Compliance = A Perfect Storm in KSA

Here’s where it gets serious. In Saudi Arabia, hiring isn’t just about finding the right person; it’s also about doing it the right way. The Personal Data Protection Law (PDPL) makes it very clear: if you’re collecting or using someone’s data, it has to be accurate, fair, and used for the right reasons. But if your AI tool is hallucinating, inventing things about candidates, that’s a problem.

For example:

• If your AI adds a skill the person never had, that’s false data.
  
  
• If it decides someone’s a bad culture fit based on their accent or name, that’s profiling.
  
  
• If it can’t explain why it made a decision, that’s a transparency issue.
  

And these aren’t just small issues. Under PDPL, mistakes like this can lead to real consequences, including fines of up to SAR 5 million. There could also be criminal penalties if sensitive data is misused. Plus, it damages trust, especially if you’re hiring for public sector roles or large enterprises.

At Recruitment Smart, we’ve built everything to avoid this from the ground up.
We don’t process data overseas; we use local data centres right here in KSA.
Our systems are explainable by design, so hiring managers can always see why a match was made. And we constantly test for bias and accuracy, because that’s not optional, it’s required. In short: if your hiring tech can’t explain itself or follow the law, it’s not safe to use. Especially here.

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4. What Hiring Managers Should Demand From Their AI Stack

If you're using AI in your hiring process, it’s okay to ask tough questions. In fact, you should.

A lot of hiring manager software or talent acquisition platforms look great on the surface, but underneath, they’re just guessing. And that’s not good enough when your decisions affect real people, compliance, and company performance.

Here’s what you should expect from your AI tools:

• Explainability
  You should always be able to see why a decision was made, not just get a score or label. If it’s a black box, it’s a risk.

Dig Deeper: Explainable AI and Human Collaboration: Enhancing Recruitment Decisions with Augmented Intelligence

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• Bias prevention built-in
  It’s not enough to “check for bias later.” Your system should be designed to avoid bias in real-time. That’s something we’ve made a priority with tools like SniperAI.

Learn More: AI Recruitment Bias Detection: Complete Compliance Guide for HR Leaders 2025

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• Data localisation
  Especially in Saudi Arabia, your candidate data should stay in the country. That’s not just safer, it’s part of staying compliant with PDPL.

How does Recruitment Smart guarantee compliance with the PDPL of Saudi Arabia?

• Confidence scoring
  If the AI isn’t sure about a match, it should say so. Smart tools don’t bluff. They highlight uncertainty so you can make the final call with full context.

Ask Yourself: Are Your Recruitment Practices Compliant?

At Recruitment Smart, we’ve made all of this part of the core tech.
We don’t just give you automation, we give you control.
And that means better shortlists, fewer surprises, and smarter decisions from the start.

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5. Time to Kill the Guesswork

At this point, one thing’s pretty clear: AI in recruitment isn’t automatically helpful. If it’s not built right, it can make things worse.

And that’s the problem we’re trying to fix. A lot of talent acquisition software out there looks sleek but hides what’s really going on behind the scenes. You get fast results, sure,  but you have no idea if those results are real or just generated guesses.

That’s not innovation. That’s gambling. In Saudi Arabia, where rules like PDPL are strict and where national goals like Saudization matter, you can’t afford to leave hiring to tools that make things up. Every mismatch hurts. Every wrong rejection sets you back.

That’s why at Recruitment Smart, we’ve built our tech, SniperAI, VScreen, and JeevesAI, to keep hallucinations out of the picture.

• SniperAI uses verified data and shows exactly why it picked a candidate.
  
• VScreen analyses interviews based on structure, not tone or assumptions.
  
• JeevesAI communicates clearly and never adds words that weren’t said.
  

Because if your recruitment system is making things up, it’s not making hiring smarter, it’s just making risk look shiny.

If you're serious about AI that’s accurate, fair, and built for how Saudi hiring works, not just how it looks on paper, let’s talk.

Book a discovery call now!