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A Unified Approach to Quantifying Algorithmic Unfairness ..:

, In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,

Speicher, Till ; Heidari, Hoda ; Grgic-Hlaca, Nina... - p. 2239-2248 , 2018

Link: https://dl.acm.org/doi/10.1145/3219819.3220046

RT 
T1 <cTitle>Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>A Unified Approach to Quantifying Algorithmic Unfairness				: Measuring Individual &Group Unfairness via Inequality Indices</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3220046&Exemplar=1&LAN=DE
				
A1 Speicher, Till
A1 Heidari, Hoda
A1 Grgic-Hlaca, Nina
A1 Gummadi, Krishna P.
A1 Singla, Adish
A1 Weller, Adrian
A1 Zafar, Muhammad Bilal
PB ACM
YR 2018
K1 algorithmic decision making
K1 fairness in machine learning
K1 fairness measures
K1 generalized entropy
K1 group fairness
K1 individual fairness
K1 inequality indices
K1 subgroup decomposability
K1 Applied computing
K1 Law, social and behavioral sciences
K1 Economics
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Supervised learning
SP 2239
OP 2248
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3219819.3220046
DO https://dl.acm.org/doi/10.1145/3219819.3220046
SF ELIB - SuUB Bremen

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