Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing

Delve deep into the crucial topic of addressing fairness issues in artificial intelligence. We explore various quantitative approaches to correcting unfair machine learning models:
Pre-processing,
In-processing and
Post-processing

Remember, fairness is a complicated issue that cannot be solved through data and algorithms alone. This is why we also discuss non-quantitative approches to fairness:
Limiting the use of ML,
Interpretability,
Explanations,
Address the root cause of unfairness,
Awareness of the problem and
Team diversity

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🚀 Companion Article (no-paywall link): 🚀
medium.com/data-science/approaches-for-addressing-…

🚀Other articles you may find useful 🚀
Introduction to Algorithm Fairness: towardsdatascience.com/what-is-algorithm-fairness-…
Reasons for Unfairness: towardsdatascience.com/algorithm-fairness-sources-…
Measuring Fairness: towardsdatascience.com/analysing-fairness-in-machi…

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