mlsanityData to 7 Oct 2026

Fairness in KI-Systemen

arXiv 2307.08486 ↗First posted 17 Jul 2023cs.LG cs.CYcited by 0

Not on the map. Why?

Abstract (arXiv)

The more AI-assisted decisions affect people's lives, the more important the fairness of such decisions becomes. In this chapter, we provide an introduction to research on fairness in machine learning. We explain the main fairness definitions and strategies for achieving fairness using concrete examples and place fairness research in the European context. Our contribution is aimed at an interdisciplinary audience and therefore avoids mathematical formulation but emphasizes visualizations and examples. -- Je mehr KI-gest"utzte Entscheidungen das Leben von Menschen betreffen, desto wichtiger ist die Fairness solcher Entscheidungen. In diesem Kapitel geben wir eine Einf"uhrung in die Forschung zu Fairness im maschinellen Lernen. Wir erkl"aren die wesentlichen Fairness-Definitionen und Strategien zur Erreichung von Fairness anhand konkreter Beispiele und ordnen die Fairness-Forschung in den europ"aischen Kontext ein. Unser Beitrag richtet sich dabei an ein interdisziplin"ares Publikum und verzichtet daher auf die mathematische Formulierung sondern betont Visualisierungen und Beispiele.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 50 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

20 of 50 references.

References · 20

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2018Datasheets for Datasetscited by 1,371
  4. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2017On Fairness and Calibrationcited by 406
  7. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  8. 2020Performative Predictioncited by 318
  9. 2018Delayed Impact of Fair Machine Learningcited by 183
  10. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  11. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  12. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
8 more
  1. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
  2. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  3. 2019Causal Modeling for Fairness in Dynamical Systemscited by 46
  4. 2022The Forgotten Margins of AI Ethicscited by 30
  5. 2021Towards the Right Kind of Fairness in AIcited by 15
  6. 2023ACROCPoLis: A Descriptive Framework for Making Sense of Fairnesscited by 8
  7. 2022Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulationcited by 6
  8. 2019Dynamic fairness - Breaking vicious cycles in automatic decision makingcited by 4

Built on by

Not cited yet.