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Fairness (machine learning)

Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).

Products, Group fairness criteria & Context

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Overview

Context

Controversies

Group fairness criteria

Individual fairness criteria

Causality-based metrics

Bias mitigation strategies

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Map overview Semantic statistics

Fairness (machine learning)

Nodes83
Edges82
Triples4
Avg. degree1.98
Density0.024096
Components1

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Important terminology

fairness displaystyle textstyle positive probability bias sensitive classifier predicted given negative formula actual protected outcome algorithm group equal quad forall

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
LinkedIninstance ofparticularly through social networks0.80text
Facebookinstance ofparticularly through social networks0.80text
gender or race.Other areas where machine learning algorithms are in use that have been shown to be biased include jobinstance ofbased on traits0.80text
loan applicationsinstance ofbased on traits0.80text

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