---
title: Algorithmic Fairness
type: concept
frequency: 42
papers_count: 42
related_concepts:
  - AI Literacy
  - Algorithmic Bias
  - Gender Bias in Large Language Models
  - Gender Bias in AI
  - Algorithmic Transparency and Explainability
tags:
  - concept
---
# Algorithmic Fairness

## Definition

Formal definierte mathematische und statistische Kriterien (z.B. Demographic Parity, Equalized Odds, Calibration) zur Messung und Sicherung gerechter Behandlung durch KI-Systeme. Das Paper problematisiert die Inkompatibilität und Nicht-Neutralität dieser Definitionen.

## Co-occurrence

| Konzept | Gemeinsame Papers |
|---------|------------------|
| [[AI Literacy]] | 13 |
| [[Algorithmic Bias]] | 11 |
| [[Gender Bias in Large Language Models]] | 4 |
| [[Gender Bias in AI]] | 3 |
| [[Algorithmic Transparency and Explainability]] | 3 |
| [[Digital Divide]] | 2 |
| [[Algorithmic Bias Mitigation]] | 2 |
| [[Representation Bias]] | 2 |

## Papers

- [[Artificial Intelligence (AI) Literacy for Social Work- Implications for Core Competencies]]
- [[Policy advice and best practices on bias and fairness in AI]]
- [[The AI literacy development canvas- Assessing and building AI literacy in organizations]]
- [[Leitfaden Digitale Verwaltung und Ethik- Praxisleitfaden für KI in der Verwaltung, Version 1.0]]
- [[A formal account of AI trustworthiness- Connecting intrinsic and perceived trustworthiness in an operational schematization]]
- [[AI literacy in K-12- a systematic literature review]]
- [[Flexible intersectional stereotype extraction (FISE)- Analyzing intersectional biases in large language models]]
- [[Data feminism for AI]]
- [[Debnath_2024_LLMs]]
- [[Imagination, Algorithms and News- Developing AI Literacy for Journalism]]
- [[AI literacy in teacher education- Empowering educators through critical co-discovery]]
- [[Revisiting Technical Bias Mitigation Strategies]]
- [[Explainable Artificial Intelligence]]
- [[Female perspectives on algorithmic bias- Implications for AI researchers and practitioners]]
- [[Bias, accuracy, and trust- Gender-diverse perspectives on large language models]]
- [[A Survey on Intersectional Fairness in Machine Learning- Notions, Mitigation, and Challenges (Gohar_2023_Survey)]]
- [[Artificial Intelligence and Intersectionality]]
- [[Artificial Intelligence and Structural Injustice- Foundations for Equity, Values, and Responsibility]]
- [[Assessing trustworthy AI- Technical and legal perspectives of fairness in AI]]
- [[Policy advice and best practices on bias and fairness in AI (Kutscher_2023_Positionings,_challenges,_)]]
- [[Artificial Intelligence Competence Needs for Youth Workers]]
- [[Artificial Intelligence in a Structurally Unjust Society]]
- [[What is AI Literacy- Competencies and Design Considerations]]
- [[Algorithms, artificial intelligence and discrimination]]
- [[Decision support and algorithmic support- The construction of algorithms and professional discretion in social work]]
- [[Advancing Accountability in AI]]
- [[DR.GAP- Mitigating bias in large language models using gender-aware prompting with demonstration and (Qiu_2025_Mitigating)]]
- [[Explainability through systematicity- The hard systematicity challenge]]
- [[AI for decision support- What are possible futures, social impacts, regulatory options, ethical conundrums and agency constellations-]]
- [[Gender Bias in Artificial Intelligence- Empowering Women Through Digital Literacy]]
- [[Investigating AI systems- examining data and algorithmic bias through hermeneutic reverse engineering]]
- [[In search of artificial intelligence (AI) literacy in teacher education- A scoping review]]
- [[Künstliche Intelligenz in der Sozialen Arbeit- Grundlagen, Entwicklungen, Herausforderungen]]
- [[Tang_2024_GenderCARE_A_Comprehensive_Framework_for]]
- [[Algorithmic discrimination- examining its types and regulatory measures with emphasis on US legal practices]]
- [[Discriminating Systems- Gender, Race, and Power in AI]]
- [[AI for impact- The PRISM framework for responsible AI in social innovation]]
- [[Bias in decision-making for AI's ethical dilemmas- A comparative study of ChatGPT and Claude]]
- [[What is Feminist AI-]]
- [[Debiasing prompts for gender bias in large language models]]
- [[Learning About AI- A Systematic Review of Reviews on AI Literacy]]
- [[Thinking like a scientist- Can interactive simulations foster critical AI literacy-]]

## Assessment-Divergenz

Von 26 bewerteten Papers: 17 Divergenzen (65%)

