We train AI on our blind spots - then call it smart

Is AI without bias a reasonable expectation?

We expect artificial intelligence to be neutral.

Unbiased. Logical. A clean slate.

But how can we expect a machine to see clearly when we refuse to look in the mirror ourselves?

From facial recognition systems that misidentify Black and Brown faces to hiring tools that replicate years of discrimination, the examples of bias in AI are stacking up like glitchy code.

Yet these aren’t random malfunctions - they’re mirrors.

AI is only as fair as the people who build it, and right now, we’re training it on our blind spots.

The bias isn’t in the machine - it’s in the mirror

When Amazon had to pull back an internal AI recruitment tool for penalising women applicants, it wasn’t the algorithm that made the decision to exclude.

It was learning from historical hiring that already existed.

The tool drew from patterns that devalued resumes with women’s names or keywords like “women’s chess club.”

This is the core truth: AI learns from us.

It scrapes data, detects patterns, and replicates them - regardless of whether those patterns were equitable or harmful.

When we say an AI system is biased, what we’re really saying is: the world we trained it on was.

And that’s not a technical flaw. That’s a leadership one.

If we can’t admit our bias, we’ll never build ethical tech

Many leaders in tech want AI to be the “solution” to human error but sidestep the uncomfortable reality that our institutions - corporate, legal, financial - are deeply biased by design.

AI just makes those biases faster, more scalable, and harder to detect.

We love to invest in fairness audits for algorithms but hesitate to examine our own performance review processes.

We call for ethical frameworks yet balk at inclusive hiring.

We want the machine to do what we haven’t done ourselves be accountable, be fair, be aware.

The truth is: bias in AI is not inevitable.

But it is predictable - when we build it without acknowledging the people and systems it reflects.

This is an inclusive leadership issue

Ethical AI development requires more than compliance - it demands courageous leadership.

Inclusive leadership isn’t just about representation on your DEI report.

It’s about embedding equity into decision-making, dataset design, product roadmaps, and innovation cycles.

It’s about asking not just what our technology can do, but who it leaves out and why.

When leaders bring lived experience, cultural competency, and critical thinking into tech creation, AI becomes more than a mirror - it becomes a tool for transformation.

But when we deny bias, ignore context, and optimise for efficiency over equity? We train AI on our blind spots - then call it smart.

The real audit starts with you

So, here’s the challenge: before you audit the algorithm, audit the organisation.

Ask:

Who was in the room when this tool was designed?

Whose data informed the decisions?

Are our systems just automating harm we’ve never been willing to address?

Smart tech isn’t just built with better code - it’s built with better values.

And that starts with inclusive, accountable, bias-aware leadership.

This op-ed was first published in CRN US's Inclusive Leadership newsletter.

Photo by Igor Omilaev on Unsplash.