The relationship between fairness and bias is, however, complex.
A suitable and timely example to discuss this complexity is affirmative action, i.e. giving preference to women, black people, or other groups that are often treated unfairly (Cambridge Dictionary).
A bias is explicitly introduced for rectifying previous unfair treatment.
The intrinsic issue of fairness is that while everyone should be treated the same, not everyone is the same.
In the case for affirmative action, origin, race, sex, the socio-economic status and the education of parents are beyond the control of a young person and but make a huge difference in the opportunities they have.
This question is currently under debate in the context of the US college admission process where affirmative action is challenged by President Trump.
Affirmative action and similar approaches suffer from the complexity of quantifying the level of preference introduced into the systems. The system of quotas which is often applied takes a very simple approach to a complex problem.
Other applicable examples are
- Tax code
What's fair a flat or a progressive tax system?
Is risk assessment fair?
Is it fair that young drivers pay more?
Is it fair to provide health insurance discounts for age, life style, body mass index?
When applying fairness and bias to AI systems, a definition of fairness needs to be provided.
The discussion between progressive, libertarian and conservatives approaches is ongoing. While some countries found broad consensus on the definition of fairness in their societies, the US is not among them.