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Bias

BY-uhs /ˈbaɪəs/

nounverbB2GRESATTOEFLIELTS

formalgeneral

(noun) Prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. (verb) Cause to feel or show inclination or prejudice for or against someone or something.

In AI, 'bias' refers to systematic errors in a model's predictions or decisions due to skewed or unrepresentative training data, or flaws in the algorithm itself. It can lead to unfair or discriminatory outcomes.

The story behind the word. This word comes from Old French `biais`, meaning 'a slant, a slope, an oblique angle.' It originally referred to a physical deviation, like the cut of cloth, and later evolved to mean a mental inclination or prejudice. In the context of AI, it perfectly describes how a system can 'slant' its decisions or predictions due to skewed data or flawed design, leading to unfair outcomes.

Word relationships

prejudice: Strongly implies a negative, preconceived opinion.
partiality: A preference for one side, not always negative.
slant: Suggests a particular perspective or angle.

impartiality: Fairness and objectivity, without prejudice.
objectivity: Lack of bias or personal feelings.

Word family

biased (adjective): Having or showing bias.
unbiased (adjective): Without bias; impartial.

Collocations

unconscious bias
gender bias
racial bias
reduce bias
systemic bias

Idioms & expressions

confirmation bias
selection bias

Example sentences

"If the training Data contains historical inequalities, the AI Model may exhibit Bias against certain demographic groups." Discussing fairness in AI.
"Critics argue that the news outlet's reporting shows a clear political Bias, favoring one party over another." A news report on media.

Memory hook

Think of a 'biased' person as having a 'slant' or 'lean' towards one side, not straight and fair. Slanted View

When not to use

Do not use 'bias' to refer to random errors or simple mistakes. It implies a consistent, systematic deviation or prejudice.

Fun facts

Algorithmic bias is a significant concern in AI ethics, as biased systems can perpetuate and amplify societal inequalities.
One famous example of AI bias involved facial recognition systems performing poorly on darker-skinned individuals due to insufficient training data.