When algorithms inherit our history, who decides what justice looks like?
Bias in AI is often described as a technical flaw — a bug to be ironed out. But this framing is misleading. Bias is not a glitch; it is a mirror. AI systems learn from data shaped by human choices, cultures, and institutions. In doing so, they reproduce the patterns — and prejudices — that already exist.
That makes fairness not just a technical challenge, but a question of governance. It forces us to ask: who defines fairness, and on whose terms?
What Does Fairness Mean?
There is no single definition. Some argue fairness means equality — treating everyone the same. Others insist it requires equity — treating people differently to achieve just outcomes. And what counts as fair in one cultural or legal setting may not be fair in another.
This makes AI governance especially tricky: a system trained in one country may be deployed globally, carrying its embedded assumptions across borders.
Governance Tools
- Audits: independent assessments to test whether systems behave consistently and fairly across groups.
- Transparency mandates: requiring organisations to explain how decisions are made, and on what data.
- Redress mechanisms: giving individuals a right to appeal when an algorithm’s decision harms them.
These tools are promising, but they raise another issue: who sets the standards, and how are they enforced?
The Risk of Fairness-Washing
Companies increasingly advertise their systems as “bias-free” or “fair by design.” Yet without shared definitions or enforceable standards, these claims can amount to little more than marketing. Fairness risks becoming a brand, not a guarantee.
Governance, then, is about more than algorithms. It is about power — who has the authority to define fairness, who benefits from those definitions, and who bears the cost when systems fail.
Why It Matters
If we treat bias as a bug, we focus only on technical fixes. If we see it as a matter of governance, we are forced to confront harder questions: whose values are built into our systems, and whose voices are excluded? Fairness in AI is not a box to tick — it is a political choice.