Doesn’t AI screening introduce more bias?

UPFLINX

This question was originally asked by one of our webinar attendees and has been shared here by the UPFLINX team to invite broader insights from the community.

Doesn’t AI screening introduce more bias; especially when it relies on keywords in CVs and might skip over strong candidates who don’t use the expected terms?

Chesta

It’s a common concern. Many recruiters worry that AI tools might reduce people to patterns, filtering out promising candidates who simply use different language to describe their skills or experience. But is this bias more or less significant than the bias a human might bring?

FlorianRocktaeschel

Bias exists in both humans and machines. A human recruiter who doesn’t follow structured evaluation criteria is highly susceptible to unconscious bias, be it based on background, age, education, or even something as subtle as tone of voice or formatting style. Humans get emotionally involved, have preferences, and - even with the best intentions - can miss good candidates simply because their story doesn’t match expectations.

AI, on the other hand, operates within predefined logic. It doesn't get tired, doesn't have mood swings, and doesn't favour a CV based on design or a familiar school name. When trained and applied properly, it can reduce some forms of subjective bias by evaluating candidates consistently. But, and this is crucial, AI is only as fair as the data and assumptions it's built on.

You’re right to point out the issue with keyword matching. Many traditional Applicant Tracking Systems (ATS) are based on simple filters: if a CV lacks a certain term, it gets excluded. That’s not intelligent screening, it’s automated exclusion.

But this is where more advanced AI tools can make a real difference. Modern AI in recruitment is moving away from hardcoded keyword rules and toward semantic search and contextual understanding. Rather than just matching words, these systems try to understand what the candidate actually did. For example, someone who writes "built real-time messaging features using asynchronous frameworks" might still be recognized as having experience with event-driven architectures, even if they never mention the word "Kafka."

Still, every system has limits. Whether it’s a human, a traditional tool, or a neural network, you can only interpret what’s actually there. If a CV lacks core information, no system, AI or otherwise, can magically infer it. That’s why AI works best as a supporting tool, not a replacement for judgment.

In our own work at UPFLINX, we’re trying to build solutions that go beyond filters. Our bots aim to interpret missing details, identify inconsistencies, and suggest relevant follow-up questions for recruiters. For instance, if a candidate doesn’t explicitly mention a skill but has projects or roles that imply it, the AI can raise that as a potential match for deeper exploration.

The future of recruitment isn’t about replacing humans with machines, it’s about combining the strengths of both. Use AI to surface overlooked potential, to create a more consistent shortlist, and to highlight interesting gaps in profiles. Then let humans do what they do best: ask the right questions, read between the lines, and make empathetic, long-term decisions.

ElenaHRstrategist

I’ve had this exact debate with several clients. There’s a widespread misconception that AI inherently introduces bias but it’s more nuanced than that.

In my work, I’ve seen human bias play out in subtle ways: unconsciously favoring candidates who went to the same university, dismissing resumes with formatting quirks, or making snap judgments based on a photo. AI doesn’t fix that automatically, but it can be trained to counteract it if done responsibly.

The danger is in relying on outdated systems. Many legacy ATS still operate on exact keyword matching, which is terrible for DEI. If someone describes their work as orchestrated cloud-based infrastructure instead of writing ‘AWS + Terraform, a rigid system won’t catch that.

Newer AI tools that use natural language processing and semantic search are better. They understand context. I tested one that highlighted candidates based on problem-solving achievements, even if they didn’t mention specific tools.

But here’s the key: AI can’t invent info. If a CV lacks detail, it’s still invisible. That’s why I always pair AI screening with human follow-up, not to override it, but to question what it missed.

Bias isn’t a tech problem it’s a human one. AI gives us the chance to audit our decisions more transparently. If we take that seriously, it can actually help us become better, fairer recruiters.