Year: 2023
Type: conference-paper
Abstract: The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to ensure algorithmic fairness. While initial fairness algorithms did not consider these limitations, recent proposals aim to achieve algorithmic fairness in classification by incorporating noisiness in protected attributes or not using protected attributes at all.
Authors: Avijit Ghosh, Pablo Kvitca, Christo Wilson
Institution: Northeastern University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W4383860239