Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact

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):

Other articles you may find useful:

Introduction to Algorithm Fairness: https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f?sk=147caf6c92b3311b9948f6361281efec

Reasons for Unfairness: https://towardsdatascience.com/algorithm-fairness-sources-of-bias-7082e5b78a2c?sk=c2491fc4e709375298671ca25a3d3ffe

Correcting Fairness: https://towardsdatascience.com/approaches-for-addressing-unfairness-in-machine-learning-a31f9807cf31?sk=67367763ce822cf60794ce7e544ba6ef

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