Suggested preparation: Introductory machine learning, statistics and data literacy.
Optional preparation: Free Responsible AI Course: Ethics, Risk and Governance.
About this course
Examine fairness as a practical and ethical problem in machine learning. The videos introduce evaluation frameworks and fairness criteria, then explore how bias can appear in data and model decisions.
Topics and lesson examples
- Introduction to Ethics in Machine Learning
- USAID Appropriate Use Framework, Exploring Fairness in Machine Learning
- Fairness Criteria, Exploring Fairness in Machine Learning
- Case Studies with Data: Mitigating Gender Bias on the UCI Adult Dataset
- Case Study: Identifying and Mitigating Unintended Demographic Bias in Machine Learning for NLP
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Intermediate — WikiFree study-level assessment |
| Language | English |
| Available lessons | 9 |
| Video duration | 1h 16m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Introductory machine learning, statistics and data literacy.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
Related courses for your next step
- Compare another approachGoogle Responsible AI for Developers: Free Fairness and Bias Course
Focus: Responsible AI reasoning · Fairness and bias assessment
Intermediate - Review a lower-level optionFree Responsible AI Course: Ethics, Risk and Governance
Focus: AI ethics · Risk-based design
Beginner
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Fairness and Ethics in Machine Learning | Intermediate | Introduction to Ethics in Machine Learning · USAID Appropriate Use Framework, Exploring Fairness in Machine Learning · Fairness Criteria, Exploring Fairness in Machine Learning | 1h 16m of video; practice time is additional |
| Google Responsible AI for Developers: Free Fairness and Bias Course | Intermediate | Responsible AI reasoning · Fairness and bias assessment · Data inspection awareness | 4 hours; individual study pace varies |
| Free Responsible AI Course: Ethics, Risk and Governance | Beginner | AI ethics · Risk-based design · Explainability | 1h 19m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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