As artificial intelligence becomes embedded in critical decisions — from loan approvals to medical diagnoses — questions of accountability and bias are moving to the centre of public debate.

AI systems learn from historical data, which means they can inherit and amplify existing human prejudices. A recruitment algorithm trained on past hiring decisions may systematically disadvantage women or ethnic minorities, not because it was designed to discriminate, but because the data it learned from reflected those biases.

Transparency is another central concern. Many AI systems operate as 'black boxes', producing outputs that even their developers struggle to explain. When a decision affects someone's job, credit score, or medical treatment, they arguably have the right to understand how that decision was made.

Governments are beginning to respond. The European Union's AI Act introduces risk-based regulation, requiring greater transparency and human oversight for high-stakes applications. But enforcement remains challenging, and liability — who is legally responsible when autonomous AI causes harm — is still deeply contested territory.

For businesses, the ethical use of AI is not just a legal question. It is increasingly a reputational one.