There are various approaches to measuring unfairness in machine learning models. We explore how to use accuracy and 3 definitions of fairness - Equal Opportunity, Equalized Odds & Disparate Impact. We shed light on the nuanced ways in which fairness can be understood in the context of algorithmic decision-making. Join us as we navigate this crucial topic, providing you with a comprehensive understanding of fairness in machine learning and its implications for building just and equitable AI systems.

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Read the companion article (no-paywall link):
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Other articles you may find useful:
Introduction to Algorithm Fairness: [ Ссылка ]
Reasons for Unfairness: [ Ссылка ]
Correcting Fairness: [ Ссылка ]

Medium: [ Ссылка ]
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