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fairness

fairness

3 5.8
A computer model that gives equal results for different groups and avoids bias
  • noun
  • /ˈfɛrnəs/
  • Specialized
translation icon : equidad
  • The company's policies aimed to improve the fairness of hiring practices by avoiding biases in candidate selection.
  • fairness perceptions
  • outcome fairness
  • perceived fairness

It includes algorithmic fairness, bias, privacy, and ethics in general.

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Examples

  • So that's why we actually have a machine learning fairness team across Google.

  • The researchers focused on ensuring the fairness of the algorithm across different demographic groups.

  • Specifically, there was no significant difference between these treatment conditions for the measure of process fairness.

    Academic text (1999)
  • So to conclude another slide on young kids, I want to make the point about equity and fairness in health.

  • In addition, perceived fairness would be significantly affected by the types of selection outcomes.

    Academic text (1992)
  • The research findings of such a program can be used to ensure fairness in assessment for a culturally diverse student body.

    Academic text (1991)
  • Years later, Emily remembered this disillusionment as an important marker in her developing awareness of fairness and justice.

    Academic text (1992)
  • However, RCT interventions did affect minority members' perceptions of process fairness.

    Academic text (1999)
  • Fairness also requires an effort, through incentives and vigorous recruitment, to achieve social-class integration of schools.

    Academic text (1992)
  • In discussions about social justice, fairness is often emphasized to promote equal opportunities for all.

Synonyms

justice
vsfairness
2.5

Fair treatment of people and the courts that decide it

applies specifically to machine learning systems avoiding systematic disadvantage across groups
equality
vsfairness
293 2.2

People have the same rights, position, and opportunities

focuses instead on balanced algorithmic outcomes across different groups rather than equal status
equity
vsfairness
  • Formal
46 5.3

Fair and equal treatment of people, for example at work or school

targets computational models and datasets to prevent systematic group disadvantage
nondiscrimination
vsfairness
4 4.2

Treating all people fairly and equally without bias

is about preventing biased outputs from algorithms across demographic groups
equitability
vsfairness
  • Formal
7.2

Being fair and not taking sides

focuses on algorithms and data giving unbiased outcomes across demographic groups
equitableness
vsfairness
  • Formal
7.4

Fair and equal treatment of people when making decisions

concerns technical systems producing unbiased results for different protected groups

Antonyms

discrimination
  • Offensive
535 3.5

Unfair treatment of people because of their race, age, or sex

bias
  • Specialized
261 5.2

A strong unfair feeling that makes you like or dislike someone and affects decisions

Compounds

People have the same rights and chances

5.3

A group that makes sure all sides get equal time to speak

How Fair

parity
26 6.5
fairness
  • Specialized
3 5.8
bias
  • Specialized
26 5.8

Surface Forms

fairness singular
fairnesses plural

Morphology

fairness = fair (transparent) = fair + ness

The word 'fairness' is transparently derived from 'fair' with the suffix '-ness' indicating a state or quality.

Etymology

In computing, fairness keeps the same idea: it means a computer program or model treats different groups 'equally' and does not give some groups a constant disadvantage.