Module 3: Algorithmic fairness in classification problems

Fairness is a controversial but important concept in automated decision making. Accuracy is not sufficient when analyzing a ML classification algorithm. Fairness metrics are used to quantify and mitigate biases in classification predictions. This module will get into a deeper analysis of techniques and its main uses in AI for classification purposes.Additionally, we will review the most relevant existing tools to operationalize fairness in a real use case.

Readings

Videos:

Arvind Narayanan (2018): 21 Fairness definitions and their politics
Solon Barocas (2017) : NIPS 2017 Tutorial on Fairness in Machine Learning

Printed materials:

Propublica COMPAS Analysis

Fairness definitions

Algorithmic Fairness in other ML settings

03.01 - Algorithmic Fairness in classification problems

04. 01 - Algorithmic Fairness in other ML settings

Case study: Titanic predictive model

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