Fairlearn: A toolkit for assessing and improving fairness in AI
By Sarah Bird, Miroslav Dud'ik, R. Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Vanessa Milan, Mehrnoosh Sameki, Hanna M. Wallach, Kathleen Walker. 2020
455 citations recorded by the source corpus.
Subfields: AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In AI Ethics, Fairness & Society
- Aram Galstyan
- Fred Morstatter
- Ninareh Mehrabi
- Kathryn Henne
- Andrew Smart
- Moritz Hardt
- P. Nicholas
- Gurleen Virk
Citations in the corpus
- Attributing Fair Decisions with Attention Interventions (2021, methodology): “as implemented in Fairlearn package (Bird et al. 2020).”
- Retiring Adult: New Datasets for Fair Machine Learning (2021, methodology): “Major software packages, such as AI Fairness 360 [7] and Fairlearn [9], expose UCI Adult as one of a few standard examples.”
- Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, background): “Bird et al. refers to these as quality-of-service harms [32].”