---
title: Intersectional Fairness
type: concept
frequency: 5
papers_count: 5
related_concepts:
  - Multicalibration
  - Subgroup Fairness
  - Fairness Gerrymandering
  - Algorithmic Bias in Language Models
  - Gender Bias in NLP
tags:
  - concept
---
# Intersectional Fairness

## Definition

Ein Fairness-Konzept in Machine Learning, das Diskriminierungsmuster an den Schnittstellen mehrerer Identitätsdimensionen (z.B. Rasse und Geschlecht) analysiert und adressiert, basierend auf Crenshaws intersektionaler Theorie. Es geht über marginalfreie Fairness einzelner Gruppen hinaus.

## Co-occurrence

| Konzept | Gemeinsame Papers |
|---------|------------------|
| [[Multicalibration]] | 2 |
| [[Subgroup Fairness]] | 2 |
| [[Fairness Gerrymandering]] | 2 |
| [[Algorithmic Bias in Language Models]] | 1 |
| [[Gender Bias in NLP]] | 1 |
| [[Algorithmic Fairness]] | 1 |
| [[Algorithmic Bias]] | 1 |
| [[Design Justice]] | 1 |

## Papers

- [[Bias and fairness in large language models- A survey]]
- [[A Survey on Intersectional Fairness in Machine Learning- Notions, Mitigation, and Challenges]]
- [[A Survey on Intersectional Fairness in Machine Learning- Notions, Mitigation, and Challenges (Gohar_2023_Survey)]]
- [[Feminist reflections for the development of Artificial Intelligence]]
- [[Intersectional Fairness- A Fractal Approach]]

## Assessment-Divergenz

Von 1 bewerteten Papers: 0 Divergenzen (0%)

