When artificial intelligence goes off course

AI bias rarely erupts as a scandal; it often quietly distorts outcomes. It can mean offers sent to the wrong people, growing frustration in customer service, or minor inaccuracies in credit decisions that gradually add up. The consequences can be significant.

In business, bias in AI leads to a slow decline in demand, reputation, and efficiency – even when dashboards look reassuringly green. That’s why bias is not just an ethical issue, but a leadership challenge. You either take control of it, or it takes control of you.

Regulatory accelerates change 

The EU AI Act came into force on 1 August 2024, with requirements phased in over the coming years. For business leaders, the key takeaway is clear: AI will become as regulated and documented as cybersecurity once was. Risk management, human oversight, and transparency are no longer optional but essential for market access.

Sanctions are serious. Breaches involving prohibited uses can lead to fines of up to €35 million or 7% of global turnover. Other obligations carry lower penalties. This isn’t alarmism – it’s the new reality. The good news? Regulation provides a solid foundation for building sustainable competitive advantage.

Where does the bias come from?

Many wonder how bias arises if no one intends it. It often stems from historical decisions and blind spots in data. For example, if a customer segment has long been marginalised, the model will learn from the old majority. As the environment changes – economy, campaigns, communication channels – the model may start treating groups differently, often unnoticed.

Simply removing “forbidden” variables isn’t enough, as similar signals can reappear indirectly. That’s why the EU AI Act allows limited processing of sensitive personal data in certain high-risk cases, specifically to detect and correct bias – under strict safeguards. It may sound counterintuitive, but you can’t lead effectively without accurate measurement.

Managing bias requires precise definitions

Managing bias is like managing quality and risk. Leadership must define fairness in real-world terms – not as an abstract ideal but as part of business objectives. When deploying a new model, consider its impact on people and fundamental rights: Who will be affected, how likely, and with what consequences? What’s the emergency stop if metrics suddenly turn red?

The AI Act makes this mandatory for public service providers, but institutionalising this approach also benefits private organisations. Once an impact assessment is complete, discussions shift from feelings to evidence, and risks become tangible and manageable.

Three key actions

In everyday practice, bias prevention involves three recurring actions.

First is visibility, which ensures that we view results from the perspectives of different customer groups, not only on paper or in development environments, but also in production.

Second, the controlled assignment of roles to people in areas with high stakes (such as credit, health, or employment) guarantees that decision-makers receive understandable justifications and can override recommendations.

Third is transparency towards stakeholders—openly communicating where AI is used, what the model is suitable for, what it is not, and under what conditions. These are not merely technical details, but essential safeguards to prevent regulatory and brand risks before they materialise.

Business leaders: Take action

Bias cannot be eliminated with a single tool, nor is it solely the concern of the data team. It’s a long-term factor in competitiveness, and managing it pays off in customer acquisition, retention, and regulatory peace of mind.

Make fairness a visible goal, conduct an impact assessment before launch, keep people in control where the stakes are highest, and ask your team how the model treats different groups. Regulation now provides a clear framework – and its phased implementation is a fact. Fortunately, the tools are ready: timely data review, clear responsibilities, and a standardised operating model. When these become part of everyday management, bias stops being a silent detriment and becomes a manageable risk.

Avoid these pitfalls

Remove all sensitive data. It sounds simple, but it leaves you blind to bias. In some limited cases, the law explicitly allows (and even directs) the use of protected data solely for measuring and correcting bias, under strict conditions and alongside data protection.

You can get everything right from the start. Bias won’t be solved in one go. You need continuous monitoring and a responsible person who can halt production if the signals change. This is precisely the governance model required by the EU framework and standards.

Share only successes? Trust is built on honesty. When you openly state where the model works and where you do not yet recommend its use, you avoid brand damage and strengthen customer relationships.

Please also read  about Voimatel's embarkation on a responsible AI journey and use of Digia's AI management model and roadmap in its development work.