5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops

Want to understand the reasons behind unfair machine learning models? We delve into the key factors that contribute to the creation of biased and unjust AI models. These are proxy variables, unbalanced samples, historical injustice in data, algorithm choice and negative feedback loops. Understanding these can help you combat biases in algorithmic decision-making.
*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/algori...
Other articles you may find useful:
Introduction to Algorithm Fairness: towardsdatascience.com/what-i...
Analysing Fairness: towardsdatascience.com/analys...
Correcting Fairness: towardsdatascience.com/approa...
Medium: / conorosullyds
Twitter: / conorosullyds
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