Black box fairness testing of machine learning models

Published: Aug 12, 2019
Abstract
Any given AI system cannot be accepted unless its trustworthiness is proven. An important characteristic of a trustworthy AI system is the absence of algorithmic bias. 'Individual discrimination' exists when a given individual different from another only in 'protected attributes' (e.g., age, gender, race, etc.) receives a different decision outcome from a given machine learning (ML) model as compared to the other individual. The current work...
Paper Details
Title
Black box fairness testing of machine learning models
Published Date
Aug 12, 2019
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