Using Artificial Intelligence To Train Your Team

Artificial Intelligence (AI) is making big waves in Learning and Development (L&D). From AI-generated training programs to bots that evaluate learner progress, L&D teams are leaning into AI to streamline and scale their programs. But here’s something we don’t talk about enough: what if the AI we are counting on is actually making things less fair? That’s where this idea of “bias in, bias out” hits home.

If biased data or flawed assumptions go into an AI system, you can bet the results are gonna be just as skewed, sometimes even worse. And in workforce training, that can mean unequal opportunities, lopsided feedback, and some learners being unintentionally shut out. So, if you are an L&D leader (or just someone trying to make learning more inclusive), let’s dive into what this really means and how we can do better.

What Does “Bias In, Bias Out” Mean Anyway?

In plain English? It means AI learns from whatever we feed it. If the historical data it’s trained on reflects past inequalities, say, men getting more promotions or certain teams being overlooked for leadership development, that’s what it learns and mimics. Imagine if you trained your LMS to recommend next-step courses based on past employee journeys. If the majority of leadership roles in your data belonged to one demographic, the AI might assume only that group is “leadership material.”

How Bias Sneaks Into AI-Driven L&D Tools

You are not imagining it; some of these platforms really do feel off. Here’s where bias often slips in:

1. Historical Baggage In The Data

Training data might come from years of performance reviews or internal promotion trends, neither of which are immune to bias. If women, people of color, or older employees weren’t offered equal development opportunities before, the AI may learn to exclude them again.

• Real talk
  If you feed a system data built on exclusion, you get… more exclusion.

2. One-Track Minds Behind The Code

Let’s be honest: not all AI tools are built by people who understand workforce equity. If your dev team lacks diversity or doesn’t consult L&D experts, the product can miss the mark for real-world learners.

3. Reinforcing Patterns Instead Of Rewriting Them

Many AI systems are designed to find patterns. But here’s the catch: they don’t know if those patterns are good or bad. So if a certain group had limited access before, the AI just assumes that’s the norm and rolls with it.

Who’s Losing Out?

The short answer? Anyone who doesn’t fit the “ideal learner” model baked into the system. That could include:

1. Women in male-dominated fields.
2. Neurodiverse employees who learn differently.
3. Non-native English speakers.
4. People with caregiving gaps in their resume.
5. Staff from historically marginalized communities.

Even worse, these people might not know they’re being left behind. The AI is not flashing a warning, it’s just quietly guiding them toward different, often less ambitious, learning paths.

Why This Should Matter To Every L&D Pro

If your goal is to create a level playing field where everyone gets the tools to grow, biased AI is a serious roadblock. And let’s be clear: this is not just about ethics. It’s about business. Biased training tools can lead to:

1. Missed talent development.
2. Decreased employee engagement.
3. Higher turnover.
4. Compliance and legal risks.

You are not just building learning programs. You are shaping careers. And the tools you choose can either open doors or close them.

What You Can Do (Right Now)

No need to panic, you have got options. Here are a few practical ways to bring more fairness into your AI-powered training:

Kick The Tires On Vendor Claims

Ask the tough questions:

1. How do they collect and label training data?
2. Was bias tested before rollout?
3. Are users of different backgrounds seeing similar outcomes?

Bring More Voices To The Table

Run pilot groups with a wide range of employees. Let them test tools and give honest feedback before you go all-in.

Use Metrics That Matter

Look beyond completion rates. Who’s actually being recommended for leadership tracks? Who’s getting top scores on AI-graded assignments? Patterns will tell you everything.

Keep A Human In The Loop

Use AI to support (not replace) critical training decisions. Human judgment is still your best defense against bad outcomes.

Educate Stakeholders

Get your leadership on board. Show how inclusive L&D practices drive innovation, retention, and brand trust. Bias in training isn’t just an L&D problem, it’s a whole company problem.

Quick Case Studies

Here’s a peek at some real-world lessons:

• Win
  A major logistics company used AI to tailor safety training modules but noticed female staff weren’t advancing past certain checkpoints. After reworking the content for broader learning styles, completion rates across genders evened out.
• Oof
  One big tech firm used AI to shortlist employees for upskilling. Turns out, their tool favored people who’d graduated from a handful of elite schools, cutting out a huge portion of diverse, high-potential talent. The tool got scrapped after pushback.

Let’s Leave It Here…

Look, AI can absolutely help L&D teams scale and personalize like never before. But it’s not magic. If we want fair, empowering workforce training, we have got to start asking better questions and putting inclusion at the center of everything we build.

So, next time you are exploring that slick new learning platform with “AI-powered” stamped all over it, remember: bias in, bias out. But if you are intentional? You can make it bias-proof.

Need help figuring out how to audit your AI tools or find vendors who get it? Drop me a note or let’s grab a coffee if you are in London. And hey, if this helped at all, share it with a fellow L&D pro!

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