When algorithms aren’t neutral: How algorithmic bias affects AI and SEO

Not all algorithms are created equal—some are built with bias baked in.

While AI tools continue to reshape how we create, optimise, and distribute content, there’s a growing problem hiding in plain sight: algorithmic bias. It doesn’t always shout—it often whispers. But those whispers can quietly influence what we see, what ranks, and who gets left out.

Algorithmic bias happens when AI systems make decisions that systematically favour one group over another—not because they’re told to, but because the data they’ve been trained on leads them there.

It’s not always the algorithm’s fault. Bias creeps in through the usual suspects:

• Skewed datasets that don’t represent everyone equally or equitably..
• Human-labelled inputs that reflect unconscious prejudice.
• Model design choices that emphasise efficiency over fairness.
• Even the questions we ask the AI can shape biased outcomes.

These aren’t just hypothetical risks. We’ve seen real-world damage:

• Facial recognition tech misidentifying people of colour at alarmingly high rates, leading to false arrests.
• Healthcare algorithms that under-prioritised patients of colour for critical care based on flawed predictive models.
• Hiring platforms trained on past resumes that learned to prefer male candidates, simply because that’s who had been hired before.

One of the landmark studies here is Gender Shades by Joy Buolamwini, which found major facial recognition systems—developed by leading tech companies—performed significantly worse on darker-skinned women than lighter-skinned men. In response, some companies have even pulled their products from the market.

Both IBM and SAP have since stepped forward with toolkits and policies to detect and reduce algorithmic bias. Their message is clear: unchecked AI can amplify human prejudice at scale.

So when we talk about algorithmic bias, we’re not talking about broken code. We’re talking about AI quietly inheriting our flaws—and spreading them faster than ever.

Most people assume search engines are neutral—that whatever ranks highest must be the most relevant. But that’s not always the case.

Search algorithms are trained on existing data, user behaviour, and engagement signals. And like any AI, they reflect the patterns—and problems—in that data. So bias doesn’t just sneak into search results; it shapes them.

Here’s how it tends to show up:

• Dominant voices get pushed to the top. Content that reflects mainstream narratives often ranks higher, while culturally diverse or community-specific content gets overlooked. Not because it lacks value—but because it doesn’t align with what the algorithm has learned to favour.

• Context gets lost across cultures. Keywords can carry different meanings in different communities, but search algorithms usually interpret them through a narrow, often Western-centric lens.

• Marginalised content is more likely to be flagged. Automated moderation systems—especially those powered by AI—can mistakenly classify Indigenous, LGBTQIA+, or racially charged content as “sensitive” or inappropriate, simply because they don’t grasp the nuance.

This matters. If your content speaks from an underrepresented perspective—or is tailored to a specific cultural experience—it may struggle to gain traction, even if it’s relevant, accurate, and well-written. And that’s more than an SEO issue—it’s a visibility issue.

When bias influences what gets surfaced and what gets buried, the web becomes an echo chamber. Loud on the familiar. Quiet on the diverse.

Algorithmic bias is not just affecting SEO and digital marketing. Generative AI tools like ChatGPT, Gemini and others are quickly becoming staples in content workflows—but they’re not as neutral as they might seem.

These large language models (LLMs) are trained on enormous datasets scraped from the internet. That means their knowledge—and their bias—comes from whatever’s already out there. The good, the bad, and the deeply flawed.

Here’s where bias tends to surface:

• Autocomplete and keyword suggestions that skew toward dominant viewpoints or commercial language, reinforcing what’s already popular instead of surfacing something new, nuanced or groundbreaking. 
• Tone and phrasing that may unintentionally preference one culture, gender, or worldview over others—simply because that’s what the model saw most during training.
• Factual representation that quietly glosses over or misrepresents certain communities, histories, or events. Not because of intent, but because of gaps in the training data provided to the model during inception. 

For marketers, the risks go beyond just awkward copy produced by generative AI:

• Biased input leads to biased output. If your AI-generated content reflects skewed assumptions, it can damage trust, alienate users, or misrepresent your brand—especially if you’re working in sensitive spaces like health, finance or education.
• Misinformation can sneak in. Even subtle inaccuracies can mislead audiences or cause compliance issues.
• You could be leaving people out. If your content doesn’t reflect the diversity of your audience, you’re not just missing the mark—you’re narrowing your reach.

The takeaway? Generative AI can save time, but it can also quietly carry bias into your content if left unchecked. And in digital marketing, what you publish is how you’re perceived.

Tackling algorithmic bias isn’t just a concern for content teams—it’s become a major focus across the tech industry. From AI developers to search giants, many are investing in ways to make their systems more fair, transparent, and accountable.

• IBM and SAP have taken the lead with tools like AI Fairness 360—an open-source toolkit designed to help developers detect and reduce bias in machine learning models. These tools offer metrics, testing frameworks, and mitigation strategies that can be applied across industries—from finance to HR to digital platforms.
• SAP’s approach includes embedding ethical guidelines into every stage of AI development, with policies designed to monitor how models behave in real-world applications—not just in training environments.
• Google has publicly committed to advancing “Responsible AI”, investing in fairness research and improving how its algorithms handle underrepresented voices and sensitive topics. While the details behind its search systems remain tightly held, it has acknowledged that reducing bias is a key part of maintaining relevance and trust.
• Microsoft has focused on building “AI for Good” principles into its platforms, with dedicated ethics teams, governance frameworks, and tools like InterpretML and Fairlearn—geared toward explainability and fairness.

Together, these efforts signal a shift: it’s no longer enough for AI to be fast or powerful. It has to be trustworthy. The rise of terms like “Responsible AI”, “Explainable AI”, and “Trustworthy Search” points to a broader industry movement—one that recognises the long-term risks of letting bias go unchecked.

The technology is still evolving. But the message from leading brands is clear: fairness can’t be an afterthought—it has to be part of the foundation.