Responsible Data Science
Build Ethical, Fair, Transparent, and Auditable Machine Learning Systems
Created by Peter Bruce, Grant Fleming
Explore how to build machine learning systems that are ethical, fair, and transparent. You'll learn to recognize risks like bias and privacy failures, and gain practical tools for auditing and improving your models. By the end, you'll be able to create data science projects with greater accountability and social awareness.
Wiley | Sep 2026 | 304 min
What You Will Learn
You'll start by examining real-world harms and ethical challenges in data science. Through structured sections, you'll move from foundational concepts to hands-on work with fairness metrics, interpretability, and bias mitigation. Practical examples using neural networks and language models help you apply these ideas to your own projects.
Key Features
- Identify and address ethical risks such as bias, discrimination, and privacy failures
- Apply fairness metrics, interpretability methods, and auditing techniques in real projects
- Document and justify modeling choices for more transparent and defensible systems
Target Audience
Ideal for data scientists, machine learning practitioners, analytics managers, and AI project leads who already understand basic modeling. If you want to reduce ethical risks and make your work more transparent, fair, and accountable, this course will help you build the skills and mindset you need.
