A large body of empirical evidence points to bias in access to credit. This is stigmatizing women, ethnic minorities and consumers from certain geographical zones. Nowadays, algorithms and machine-learning techniques could be unintentionally exacerbating this bias. HEC Associate Dean for Research, Christophe Pérignon, describes these challenges - and new techniques he has developed to reduce bias impact. These techniques can be applied in banking, insurance, hiring, fraud detection and justice. Hosted on Acast. See acast.com/privacy for more information.

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