Ethics and Privacy
Throughout the book, we have learned several techniques for taking data and turning it into insights. Like any powerful tool, these can be used for good or bad, and can be used in bad ways unintentionally. For example, we could accidentally leak private data through machine learning (ML) algorithms or data engineering pipelines. Part of being a well-rounded data scientist means we need to understand the ethical considerations of doing data science. Quite often this includes aspects of privacy and bias. We will learn more about these ethical and privacy considerations in this chapter, including:
- Bias in machine learning algorithms
- Data privacy considerations in data preparation and analysis
- How to use k-anonymity and l-diversity to protect people's data privacy
- Data privacy laws and regulations
- Using data science for the common good
Since we've recently covered machine learning in the book, let's start by...
