Fairness, Bias & Explainability in AI

AI systems influence real people, decisions and opportunities. Fairness is not a technical detail — it is a cornerstone of legitimacy, equality and public trust. Organisations must ensure that AI outcomes are understandable, justifiable and free from unjustified discrimination.

What Fairness Means in AI

Fairness refers to the absence of unjustified differences in outcomes between groups. What is considered “fair” depends heavily on context, societal norms and the goals of the system.

Types of Bias

• Dataset Bias: skewed or incomplete training data that disadvantages specific groups
• Model Bias: behaviour of the AI model that amplifies differences
• Contextual Bias: using a model in a context different from what it was designed for
• User Bias: misinterpretation of AI outputs by humans

Why Explainability Matters

Without explainability, users cannot understand or contest AI decisions. Transparency supports trust, effective oversight and legal compliance — and is required under the EU AI Act.

Transparency Requirements Under the AI Act

• Clear explanations of model behaviour and limitations
• Documentation of risks, assumptions and mitigation steps
• Traceability of data and decision-making
• Human oversight and intervention possibilities

How Organisations Can Ensure Fairness

• Measure bias using fairness metrics
• Use datasheets & model cards for documentation
• Engage stakeholders and diverse user groups
• Define clear oversight and escalation processes
• Implement explainability for end-users, not only developers

Fairness and explainability are at the core of responsible AI — they determine whether AI systems support public values and uphold human rights.