# ML crash course - Fairness

> Machine learning crash course 중 "Fairness" 챕터

[Machine learning crash course](https://wiki.g15e.com/pages/Machine%20learning%20crash%20course.txt) 중 "Fairness" 챕터

https://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