Algorithmic Bias
AI & Emerging Tech
When an automated system treats groups of people unfairly because of what it learned from.
In practice
Algorithmic bias is systematic unfairness in a model's outputs — hiring tools that downgrade women, recognition that fails on darker skin, credit models that echo old discrimination — usually inherited from biased training data or from what the designers chose to measure. It is a design problem as much as a data one, because someone decided what the system would optimise.
Questions
What is Algorithmic Bias?
Algorithmic bias is when a system's decisions are consistently skewed against particular people, because the data or the objective it was built on carried that skew.
Why do you use Algorithmic Bias?
Because these systems scale a decision to millions of people at once. A bias that would be one person's prejudice becomes institutional the moment it is automated.
How do you use/apply Algorithmic Bias?
Audit outputs across groups before shipping, test with the people most likely to be harmed, question what the system is optimising for, and keep a human route to appeal.
When do you use Algorithmic Bias?
Before any model that affects access — to jobs, money, housing, health — goes near real people. And again after, continuously.
Who uses Algorithmic Bias?
Every designer and team shipping a model that decides something about a person.
