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Artificial Intelligence & Machine Learning vocabulary · Bias, Safety and Responsible AI

Bias

Meaning in artificial intelligence & machine learning, with examples

/ˈbaɪəs/nounCore term

In artificial intelligence & machine learning, bias means: Systematic unfairness in a model's outputs, for example working worse for some groups of people, usually caused by skewed data or design choices.

In Turkish: önyargı, yanlılık

How it is used

Teams detect, measure, audit and reduce bias, and describe it in model cards. Common phrases are 'gender bias', 'data bias' and 'bias in hiring models'. In technical papers the word can also have a purely mathematical meaning, so check the context.

Example sentences

  1. The audit found a clear bias: the model rejected older applicants more often.

  2. We added more speakers with regional accents to reduce bias in speech recognition.

Common collocations

  • reduce bias
  • measure bias
  • gender bias
  • data bias
  • bias audit
  • fairnessThe goal that a model treats different groups of people equitably, for example by giving similar error rates or approval rates across genders, ages or regions.
  • explainabilityHow well people can understand why a model made a particular prediction or decision, often with the help of tools that show which inputs mattered most.
  • training dataThe examples a model learns from during training. Their quality, size and variety largely decide how well the model works on new data.
  • alignmentMaking an AI system's behavior match human intentions and values, so that it is helpful and honest and avoids causing harm. Also the research field that studies this.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Bias, Safety and Responsible AI

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