Principles of Data Science Ethics

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, those who developed and deployed these algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.

This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies along with Python code are provided.

What you'll learn

• After completing this course you should be able to:
• Identify and anticipate the types of unintended harm that can arise from AI models
• Explain why interpretability is key to avoiding harm
• Distinguish between intrinsically interpretable models and black box models
• Evaluate tradeoffs between model performance and interpretability
• Establish a Responsible Data Science framework for your projects