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.
*NOTE*: You will now get the XAI course for free if you sign up (not the SHAP course)
SHAP course: adataodyssey.com/courses/shap...
XAI course: adataodyssey.com/courses/xai-...
Newsletter signup: mailchi.mp/40909011987b/signup
Read the companion article (no-paywall link):
towardsdatascience.com/analys...
Other articles you may find useful:
Introduction to Algorithm Fairness: towardsdatascience.com/what-i...
Reasons for Unfairness: towardsdatascience.com/algori...
Correcting Fairness: towardsdatascience.com/approa...
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Mastodon: sigmoid.social/@conorosully
Website: adataodyssey.com/

[@adataodyssey]

*NOTE*: You will now get the XAI course for free if you sign up (not the SHAP course)
SHAP course: adataodyssey.com/courses/shap-with-python/
XAI course: adataodyssey.com/courses/xai-with-python/
Newsletter signup: mailchi.mp/40909011987b/signup

[@ogmarekianko6072]

Excellent content, keep it up!

[@adataodyssey]

Thank you :) will do!