Artificial intelligence has quickly become the dominant topic in hiring conversations, overtaking diversity, equity and inclusion (DE&I) as the top priority for many HR and talent teams. But in the rush to implement AI-powered tools, we risk overlooking the deep connection between these two areas.

AI is no longer just a futuristic concept; it’s already shaping who gets shortlisted, interviewed, or rejected. That brings incredible efficiency. But it also introduces new risks, especially when the systems we rely on are trained on historical data that may carry the very biases we're trying to eliminate.

The Compliance Conversation

With new regulations emerging in both the US and Europe, much of the current focus is on compliance. Tools that automate decision-making in hiring are coming under greater scrutiny, and rightly so. Transparency, explainability, and auditability are becoming legal and ethical necessities. But regulation alone won’t solve the core issue. Following the letter of the law doesn’t guarantee that we’re building fairer systems. It just ensures we’re staying within the rules.

The bigger question is this:

Are we using AI to build fairer hiring processes or just faster ones?

What’s Going on Under the Hood?

From what we’re seeing, a significant number of organisations are adopting AI in recruitment without fully understanding what’s going on under the hood. How does the algorithm make decisions? What data is it trained on? How are outcomes audited for bias?

This opacity is where bias can hide—and scale.

Without a clear grasp of how these tools work, it’s easy to fall into a false sense of security. We assume that because a decision was made by a machine, it must be objective. But that’s not how machine learning works. If past data reflects unequal opportunity, the AI will likely reinforce those patterns, not remove them.

The Intersection of AI and DE&I

The intersection between AI and DE&I is still widely underexplored. In theory, AI could help mitigate human bias, widen talent pools, and improve access to opportunity. But in practice, the impact depends entirely on how these systems are built, trained, and used.

Fairness isn’t a default setting. It has to be designed in, tested for, and constantly monitored.

As the field evolves, we should be asking ourselves:

 - How do we ensure AI tools align with our DE&I goals?

 - What mechanisms are in place to catch unintended bias?

 - Are we creating more transparency or more confusion?

Moving Forward with Intentionality

We’re only scratching the surface of how AI will shape the future of hiring. To truly harness its potential, we need to move beyond speed and scale, and focus on intentionality. That means understanding the tools we use, questioning the data they rely on, and ensuring fairness is built into every stage of the recruitment process.

Because if we’re not careful, we might just end up digitising and accelerating the very problems we set out to solve.