Fairness

How it is used

Teams evaluate fairness with metrics that compare groups and report it in model cards and audits. There are several definitions of fairness and they can't always be met at the same time, so the choice depends on the use case and the law.

Example sentences

1. Before launch, we checked fairness across age groups and regions.

2. The fairness review found that error rates were twice as high for one dialect.

Common collocations

• fairness metrics
• evaluate fairness
• fairness audit
• group fairness

Related terms

• biasSystematic unfairness in a model's outputs, for example working worse for some groups of people, usually caused by skewed data or design choices.
• 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.
• 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.
• class imbalanceA situation in which some classes in a dataset have far more examples than others, for example 990 normal transactions for every 10 fraudulent ones.

This word in other fields

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

• fairness · Artificial Intelligence & Machine Learning

More terms: Machine Learning and Data Processing

• artificial intelligence
• machine learning
• model
• dataset
• training data
• label
• train
• supervised learning
• neural network
• deep learning
• loss function
• overfitting