<span style="font-variant: small-caps">FairCaipi</span>: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing bias-mitigation strategies adapt the importance of...
Main Authors: | Louisa Heidrich, Emanuel Slany, Stephan Scheele, Ute Schmid |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Machine Learning and Knowledge Extraction |
Subjects: | |
Online Access: | https://www.mdpi.com/2504-4990/5/4/76 |
Similar Items
-
Addressing Bias in Machine Learning Algorithms: Promoting Fairness and Ethical Design
by: Dhabliya Dharmesh, et al.
Published: (2024-01-01) -
Privacy-preserving federated machine learning on FAIR health data: A real-world application
by: A. Anil Sinaci, et al.
Published: (2024-12-01) -
Fair Method for Spectral Clustering to Improve Intra-cluster Fairness
by: XU Xia, ZHANG Hui, YANG Chunming, LI Bo, ZHAO Xujian
Published: (2023-02-01) -
A fair evaluation of the potential of machine learning in maritime transportation
by: Xi Luo, et al.
Published: (2023-07-01) -
Integrating Fairness in the Software Design Process: An Interview Study With HCI and ML Experts
by: Seamus Ryan, et al.
Published: (2023-01-01)