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Weekly Newsletter Vol. 111 - October 15, 2025

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AI & Algorithmic Bias — Not Just What AI Can Do, But How It Might Be Unfair

Over the past few years, AI has quietly become one of the most influential players in the hiring process. From resume-screening to interview scheduling and even candidate assessments, automation has taken center stage. According to recent insights from SHRM and Korn Ferry, over 40% of companies now use AI in at least one stage of recruitment — a number that continues to rise.

But as adoption accelerates, a growing body of research is raising an uncomfortable truth: Many AI tools are not neutral. They often reflect and sometimes amplify the same human biases they were designed to eliminate.

How Bias Sneaks In

Recent studies show that AI models trained on massive datasets can inadvertently learn patterns that reflect historical or cultural inequities.

For example:

Cultural or linguistic bias: Large language models sometimes rate candidates who are non-native English speakers or use regionally distinct phrasing as less “competent” or “professional,” even when resumes are anonymized.

Gender or socioeconomic bias: Some resume-screening algorithms still favour candidates with traditionally “male-coded” experiences or degrees from more expensive universities.

“LLM self-preference bias”: A fascinating (and troubling) new phenomenon — if a candidate uses AI (like ChatGPT or similar tools) to write their resume, and the employer uses that same model to evaluate it, the model may prefer its own writing style, giving that applicant an unintended advantage.

This isn’t science fiction, it’s happening right now. And it challenges the fairness we expect in hiring.

Why This Matters More Than Ever

This isn’t just a technical issue, it’s a human issue that shapes who gets opportunities and who doesn’t.

Fairness & Diversity: Bias in AI can disproportionately exclude qualified candidates from diverse backgrounds undermining DEI efforts that companies have worked hard to build.

Employer Brand: Candidates today expect transparency. If they sense the process is opaque or unfair, they’re less likely to reapply or recommend your company.

Regulatory Pressure: From Ontario to New York to the EU, new legislation is already targeting AI accountability in hiring. Organizations that fail to audit their systems could soon face compliance risks.

What Needs More Attention

If AI is here to stay and it is then the focus needs to shift from “What AI can do” to “How AI should behave.”

Here are four key areas where hiring teams, tech providers, and HR leaders can lead the way:

Transparent Communication 

Be upfront with candidates about where and how AI is used in the hiring process. Let them know if initial screenings or assessments involve algorithmic tools, and offer the option for human review. As of January 1st, 2026, companies in Ontario will need to share how they use AI in their hiring process right in the job descriptions.

Regular Auditing for Bias

Test and audit your tools regularly. Partner with external experts or diversity councils to check for disparities in how resumes, interviews, or assessments are scored.

Human Oversight at Key Decision Points 

Automation should assist, not replace, recruiters. Having humans review edge cases or unclear outcomes ensures fairness — and maintains the human connection candidates value.

Designing for Cultural & Linguistic 

Awareness Tools should be built and trained to recognize that not everyone writes, speaks, or presents in the same way — and that’s a strength, not a flaw.

The Future of Fair Hiring

AI has the potential to make hiring smarter, faster, and more inclusive if it’s built and managed responsibly. As recruiters, leaders, and hiring professionals, our role isn’t just to adopt the newest tools, it’s to question how those tools shape outcomes for real people.

The goal isn’t to remove bias completely but it’s to recognize it, measure it, and continually improve.

And maybe that’s the biggest opportunity of all: Not letting AI replace human judgment but using it to make our judgment more human.

You can’t make this stuff up

_{(*Disclaimer: these are real experiences provided by RME's community of job seekers and hiring managers)}

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