Fairness Meets Cross-Domain Learning: A Benchmark of Models and Metrics
Author:
Affiliation:
1. Dipartimento di Automatica ed Informatica (DAUIN), Politecnico di Torino, Turin, Italy
2. School of Informatics, The University of Edinburgh, Edinburgh, U.K.
Funder
ELSA - European Lighthouse on Secure and Safe AI
European Union Next-GenerationEU (Piano Nazionale di Ripresa E Resilienza (PNRR)) DM 351 on Trustworthy AI
FAIR - Future Artificial Intelligence Research and received funding from the European Union Next-GenerationEU
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10487929.pdf?arnumber=10487929
Reference93 articles.
1. Gender shades: Intersectional accuracy disparities in commercial gender classification;Buolamwini
2. Fairness in Criminal Justice Risk Assessments: The State of the Art
3. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations;Seyyed-Kalantari;Nature Med.,2021
4. Dissecting racial bias in an algorithm used to manage the health of populations
5. Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
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