Discrimination in machine learning algorithms

Autor: Roberta Pappadà, Francesco Pauli
Přispěvatelé: Antonino Abbruzzo, Eugenio Brentari, Marcello Chiodi, Davide Piacentino, Pappada', Roberta, Pauli, Francesco
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Popis: 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.
Databáze: OpenAIRE