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This paper addresses the challenge of measuring group fairness in algorithmic models without access to sensitive demographic attributes. It proposes a quantification-based approach to evaluate the fairness of classifiers in various settings, particularly focusing on the effects of unawareness regarding sensitive attributes like race or sex. The study introduces several quantification methods and compares their performance across different experimental protocols.
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The Journal of Artificial Intelligence is a scholarly publication that focuses on research, developments, and applications related to artificial intelligence and related technologies.
Researchers, professionals, and organizations working in the field of artificial intelligence are typically required to submit their findings and papers to the Journal of Artificial Intelligence.
To fill out the Journal of Artificial Intelligence, authors must follow specific submission guidelines which usually include formatting their papers according to the journal's style, including an abstract, keywords, and relevant sections for methodology, results, and conclusions.
The purpose of the Journal of Artificial Intelligence is to disseminate high-quality research, facilitate the exchange of ideas, and advance knowledge in the field of artificial intelligence.
Authors must report their research findings, methodologies, data analysis, and conclusions, as well as provide appropriate citations and references.
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