ML crash course - Fairness

Machine learning crash course 중 “Fairness” 챕터

developers.google.com/machine-learning/crash-course/fairness

Introduction

Types of bias

Reporting bias

Historical bias

Automation bias

Selection bias

Group attribution bias

Implicit Bias

Confirmation bias

Experimenter’s bias

Identifying bias

Missing feature values

Unexpected feature values

Data skew

Mitigating bias

Augmenting the training data

Adjusting the model’s optimization function

Evaluating for bias

Demographic parity

Benefits and Drawbacks

Equality of opportunity

Benefits and Drawbacks

Counterfactual fairness

Benefits and drawbacks

Programming exercise

https://developers.google.com/machine-learning/crash-course/fairness/programming-exercise