Fairness and Bias in Machine Learning (Part 1)

Author(s): Lorenzo Pastore

Originally published on Towards AI.

Photo by John Schnobrich on UnsplashFainess in Machine LearningEvidence of the problemFundamental concept: Discrimination, Bias, and Fairness

Machine learning algorithms substantially affect everyday life in areas such as education, employment, advertising, and policing. While machine learning (ML) algorithms may seem objective, the tendency to favour bias is embedded in ML essence. The widespread use of ML in a variety of sensitive fields fosters the idea that ML-based decisions are based only on facts and are not affected by human cognitive biases, discriminatory tendencies, or emotions. As matter of fact, these systems learn from data which are, directly or indirectly, shaped by… Read the full blog for free on Medium.

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Published via Towards AI