AI Vocabulary · 07 When It Goes Wrong
What is Bias?
Systematic unfairness in AI output, inherited from data or design choices.
Bias in AI means outputs that are systematically skewed: certain groups described with stereotypes, recognised less accurately or treated worse. It mostly comes from training data that reflects existing inequalities, and from human choices about what to measure, how to label and where to use the system.
Well-known examples include face recognition that works worse for darker skin and hiring tools that favoured male candidates. In generative AI, bias shows up in images, wording and assumptions, such as "the CEO" being drawn as a man by default.
Why it matters for you
If AI touches decisions about people, such as hiring, credit, pricing, support priority or performance, bias can become unfair treatment and legal risk. Many of these uses count as high-risk AI in the EU.
Own it
Test outputs for different groups before you rely on them, keep a person responsible for decisions about people, and document what you checked. Bias can't be removed completely, but it can be noticed and limited.
Where it comes from
Concern about unfair software is older than modern AI. In 1996, Batya Friedman and Helen Nissenbaum published Bias in Computer Systems, describing how bias enters through existing social attitudes, technical limits and new contexts of use. The topic reached a wide audience in 2018 with Joy Buolamwini and Timnit Gebru's Gender Shades study, which showed commercial face analysis performing far worse on darker-skinned women.
Related
Your AI Learning Hub
Learn the whole AI Vocabulary, step by step, and build with it. Build it. Own it.