Machine learning algorithms are routinely used for business decisions which may directly affect individuals: for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (sex, race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.
Discrimination in machine learning algorithms
Roberta Pappadà;Francesco Pauli
2018-01-01
Abstract
Machine learning algorithms are routinely used for business decisions which may directly affect individuals: for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (sex, race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.File in questo prodotto:
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Pappada Pauli_Discrimination in machine learning algorithms.pdf
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