Review of data-driven bias: analysis of concepts for fairness audits in the regulation of high-risk AI systems

Bibliographic information

Title:  Review of data-driven bias: analysis of concepts for fairness audits in the regulation of high-risk AI systems. 

Written by:  J. Grenzebach, T. Radüntz

in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024. 4th Workshop on Bias and Fairness in AI, 2024.  pages: 1-16, Project number: F 2602

Further Information

Research Project

Project numberF 2602 StatusOngoing Project Metrics for measuring data characteristics in the training of high-risk AI systems: Understanding, predicting, and mitigating bias in digital work systems harming fundamental rights

To the Project

Research ongoing